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[{"id":"abdillIntegration168000Samples2025","abstract":"The factors shaping human microbiome variation are a major focus of biomedical research. While other fields have used large sequencing compendia to extract insights requiring otherwise impractical sample sizes, the microbiome field has lacked a comparably sized resource for the 16S rRNA gene amplicon sequencing commonly used to quantify microbiome composition. To address this gap, we processed 168,464 publicly available human gut microbiome samples with a uniform pipeline. We use this compendium to evaluate geographic and technical effects on microbiome variation. We find that regions such as Central and Southern Asia differ significantly from the more thoroughly characterized microbiomes of Europe and Northern America and that composition alone can be used to predict a sample’s region of origin. We also find strong associations between microbiome variation and technical factors such as primers and DNA extraction. We anticipate this growing work, the Human Microbiome Compendium, will enable advanced applied and methodological research.","accessed":{"date-parts":[["2025",5,5]]},"author":[{"family":"Abdill","given":"Richard J."},{"family":"Graham","given":"Samantha P."},{"family":"Rubinetti","given":"Vincent"},{"family":"Ahmadian","given":"Mansooreh"},{"family":"Hicks","given":"Parker"},{"family":"Chetty","given":"Ashwin"},{"family":"McDonald","given":"Daniel"},{"family":"Ferretti","given":"Pamela"},{"family":"Gibbons","given":"Elizabeth"},{"family":"Rossi","given":"Marco"},{"family":"Krishnan","given":"Arjun"},{"family":"Albert","given":"Frank W."},{"family":"Greene","given":"Casey S."},{"family":"Davis","given":"Sean"},{"family":"Blekhman","given":"Ran"}],"citation-key":"abdillIntegration168000Samples2025","container-title":"Cell","container-title-short":"Cell","DOI":"10.1016/j.cell.2024.12.017","ISSN":"0092-8674","issue":"4","issued":{"date-parts":[["2025",2,20]]},"note":"Read_Status: New\nRead_Status_Date: 2025-05-05T07:37:00.292Z","page":"1100-1118.e17","source":"ScienceDirect","title":"Integration of 168,000 samples reveals global patterns of the human gut microbiome","type":"article-journal","URL":"https://www.sciencedirect.com/science/article/pii/S0092867424014302","volume":"188"},{"id":"abramovStructureKnowsBest","author":[{"family":"Abramov","given":"Kesem"},{"family":"Biton","given":"Barry"},{"family":"Galai","given":"Geut"},{"family":"Puzis","given":"Rami"},{"family":"Pilosof","given":"Shai"}],"citation-key":"abramovStructureKnowsBest","language":"en","note":"Read_Status: New\nRead_Status_Date: 2025-12-01T08:50:29.812Z","source":"Zotero","title":"Structure knows best: predicting ecological interactions across space through pairwise integration of latent network patterns","type":"article-journal"},{"id":"AccueilMIAParisSaclay","accessed":{"date-parts":[["2023",7,3]]},"citation-key":"AccueilMIAParisSaclay","title":"Accueil | MIA Paris-Saclay","type":"webpage","URL":"https://mia-ps.inrae.fr/"},{"id":"acerbiVariationalBayesianMonte2020","abstract":"Variational Bayesian Monte Carlo (VBMC) is a recently introduced framework that uses Gaussian process surrogates to perform approximate Bayesian inference in models with black-box, non-cheap likelihoods. In this work, we extend VBMC to deal with noisy log-likelihood evaluations, such as those arising from simulation-based models. We introduce new `global' acquisition functions, such as expected information gain (EIG) and variational interquantile range (VIQR), which are robust to noise and can be efficiently evaluated within the VBMC setting. In a novel, challenging, noisy-inference benchmark comprising of a variety of models with real datasets from computational and cognitive neuroscience, VBMC+VIQR achieves state-of-the-art performance in recovering the ground-truth posteriors and model evidence. In particular, our method vastly outperforms `local' acquisition functions and other surrogate-based inference methods while keeping a small algorithmic cost. Our benchmark corroborates VBMC as a general-purpose technique for sample-efficient black-box Bayesian inference also with noisy models.","accessed":{"date-parts":[["2025",11,24]]},"author":[{"family":"Acerbi","given":"Luigi"}],"citation-key":"acerbiVariationalBayesianMonte2020","DOI":"10.48550/arXiv.2006.08655","issued":{"date-parts":[["2020",10,19]]},"note":"Read_Status: New\nRead_Status_Date: 2025-11-24T16:00:02.140Z","number":"arXiv:2006.08655","publisher":"arXiv","source":"arXiv.org","title":"Variational Bayesian Monte Carlo with Noisy Likelihoods","type":"article","URL":"http://arxiv.org/abs/2006.08655"},{"id":"AdvantageDisadvantage5th","abstract":"Rules describing the advantage and disadvantage system from the 5th Edition (5e) SRD (System Reference Document).","accessed":{"date-parts":[["2024",4,13]]},"citation-key":"AdvantageDisadvantage5th","language":"en","title":"Advantage and Disadvantage - 5th Edition SRD","type":"webpage","URL":"https://5thsrd.org/rules/advantage_and_disadvantage/"},{"id":"agarwalReincarnatingReinforcementLearning2022","abstract":"Learning tabula rasa, that is without any prior knowledge, is the prevalent workflow in reinforcement learning (RL) research. However, RL systems, when applied to large-scale settings, rarely operate tabula rasa. Such large-scale systems undergo multiple design or algorithmic changes during their development cycle and use ad hoc approaches for incorporating these changes without re-training from scratch, which would have been prohibitively expensive. Additionally, the inefficiency of deep RL typically excludes researchers without access to industrial-scale resources from tackling computationally-demanding problems. To address these issues, we present reincarnating RL as an alternative workflow or class of problem settings, where prior computational work (e.g., learned policies) is reused or transferred between design iterations of an RL agent, or from one RL agent to another. As a step towards enabling reincarnating RL from any agent to any other agent, we focus on the specific setting of efficiently transferring an existing sub-optimal policy to a standalone value-based RL agent. We find that existing approaches fail in this setting and propose a simple algorithm to address their limitations. Equipped with this algorithm, we demonstrate reincarnating RL's gains over tabula rasa RL on Atari 2600 games, a challenging locomotion task, and the real-world problem of navigating stratospheric balloons. Overall, this work argues for an alternative approach to RL research, which we believe could significantly improve real-world RL adoption and help democratize it further. Open-sourced code and trained agents at https://agarwl.github.io/reincarnating_rl.","accessed":{"date-parts":[["2024",2,5]]},"author":[{"family":"Agarwal","given":"Rishabh"},{"family":"Schwarzer","given":"Max"},{"family":"Castro","given":"Pablo Samuel"},{"family":"Courville","given":"Aaron"},{"family":"Bellemare","given":"Marc G."}],"citation-key":"agarwalReincarnatingReinforcementLearning2022","DOI":"10.48550/arXiv.2206.01626","issued":{"date-parts":[["2022",10,4]]},"number":"arXiv:2206.01626","publisher":"arXiv","source":"arXiv.org","title":"Reincarnating Reinforcement Learning: Reusing Prior Computation to Accelerate Progress","title-short":"Reincarnating Reinforcement Learning","type":"article","URL":"http://arxiv.org/abs/2206.01626"},{"id":"AgglomerativeNestingProgram1990","abstract":"The prelims comprise: Short Description of the Method How to Use the Program AGNES Examples More on the Algorithm and the Program Related Methods and References","accessed":{"date-parts":[["2024",9,13]]},"citation-key":"AgglomerativeNestingProgram1990","container-title":"Finding Groups in Data","DOI":"10.1002/9780470316801.ch5","ISBN":"978-0-470-31680-1","issued":{"date-parts":[["1990"]]},"language":"en","page":"199–252","publisher":"John Wiley & Sons, Ltd","section":"5","source":"Wiley Online Library","title":"Agglomerative Nesting (Program AGNES)","type":"chapter","URL":"https://onlinelibrary.wiley.com/doi/abs/10.1002/9780470316801.ch5"},{"id":"agterbergJointSpectralClustering2025","abstract":"Modern network datasets are often composed of multiple layers, resulting in collections of networks over the same set of vertices but with potentially different connectivity patterns on each network. These data require models and methods that are flexible enough to capture local and global differences across the networks while at the same time being parsimonious and tractable to yield computationally efficient and theoretically sound solutions that are capable of aggregating information across the networks. This paper considers the multilayer degree-corrected stochastic blockmodel, where a collection of networks shares the same community structure, but degree corrections and block connection probability matrices are permitted to be different. We establish the identifiability of this model and propose a spectral clustering algorithm. Our theoretical results demonstrate that the misclustering error rate of the algorithm improves exponentially with multiple network realizations, even in the presence of significant layer heterogeneity. Simulation studies show that this approach improves on existing multilayer community detection methods in this challenging regime. Furthermore, in a case study of US airport data through January 2016 – September 2021, we find that this methodology identifies meaningful community structure and trends in airport popularity influenced by pandemic impacts on travel. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.","accessed":{"date-parts":[["2025",9,19]]},"author":[{"family":"Agterberg","given":"Joshua"},{"family":"Lubberts","given":"Zachary"},{"family":"Arroyo","given":"Jesús"}],"citation-key":"agterbergJointSpectralClustering2025","container-title":"Journal of the American Statistical Association","DOI":"10.1080/01621459.2025.2516201","ISSN":"0162-1459","issued":{"date-parts":[["2025",4]]},"page":"1–15","publisher":"ASA Website","title":"Joint Spectral Clustering in Multilayer Degree-Corrected Stochastic Blockmodels","type":"article-journal","URL":"https://doi.org/10.1080/01621459.2025.2516201"},{"id":"agterbergJointSpectralClustering2025a","abstract":"Modern network datasets are often composed of multiple layers, resulting in collections of networks over the same set of vertices but with potentially different connectivity patterns on each network. These data require models and methods that are flexible enough to capture local and global differences across the networks while at the same time being parsimonious and tractable to yield computationally efficient and theoretically sound solutions that are capable of aggregating information across the networks. This paper considers the multilayer degree-corrected stochastic blockmodel, where a collection of networks shares the same community structure, but degree corrections and block connection probability matrices are permitted to be different. We establish the identifiability of this model and propose a spectral clustering algorithm. Our theoretical results demonstrate that the misclustering error rate of the algorithm improves exponentially with multiple network realizations, even in the presence of significant layer heterogeneity. Simulation studies show that this approach improves on existing multilayer community detection methods in this challenging regime. Furthermore, in a case study of US airport data through January 2016 – September 2021, we find that this methodology identifies meaningful community structure and trends in airport popularity influenced by pandemic impacts on travel. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.","accessed":{"date-parts":[["2025",9,19]]},"author":[{"family":"Agterberg","given":"Joshua"},{"family":"Lubberts","given":"Zachary"},{"family":"Arroyo","given":"Jesús"}],"citation-key":"agterbergJointSpectralClustering2025a","container-title":"Journal of the American Statistical Association","DOI":"10.1080/01621459.2025.2516201","ISSN":"0162-1459","issue":"0","issued":{"date-parts":[["2025",4]]},"note":"Read_Status: New\nRead_Status_Date: 2025-09-19T13:53:26.541Z","page":"1–15","publisher":"ASA Website","source":"Taylor and Francis+NEJM","title":"Joint Spectral Clustering in Multilayer Degree-Corrected Stochastic Blockmodels","type":"article-journal","URL":"https://doi.org/10.1080/01621459.2025.2516201","volume":"0"},{"id":"aitchisonConciseGuideCompositional","author":[{"family":"Aitchison","given":"John"}],"citation-key":"aitchisonConciseGuideCompositional","language":"en","note":"Read_Status: New\nRead_Status_Date: 2026-05-07T13:07:55.461Z","source":"Zotero","title":"A Concise Guide to Compositional Data Analysis","type":"article-journal"},{"id":"aitchisonStatisticalAnalysisCompositional1982","abstract":"The simplex plays an important role as sample space in many practical situations where compositional data, in the form of proportions of some whole, require interpretation. It is argued that the statistical analysis of such data has proved difficult because of a lack both of concepts of independence and of rich enough parametric classes of distributions in the simplex. A variety of independence hypotheses are introduced and interrelated, and new classes of transformed-normal distributions in the simplex are provided as models within which the independence hypotheses can be tested through standard theory of parametric hypothesis testing. The new concepts and statistical methodology are illustrated by a number of applications.","accessed":{"date-parts":[["2025",5,7]]},"author":[{"family":"Aitchison","given":"J."}],"citation-key":"aitchisonStatisticalAnalysisCompositional1982","container-title":"Journal of the Royal Statistical Society. Series B (Methodological)","ISSN":"0035-9246","issue":"2","issued":{"date-parts":[["1982"]]},"note":"Read_Status: New\nRead_Status_Date: 2025-05-07T07:43:11.308Z","page":"139–177","publisher":"[Royal Statistical Society, Oxford University Press]","source":"JSTOR","title":"The Statistical Analysis of Compositional Data","type":"article-journal","URL":"https://www.jstor.org/stable/2345821","volume":"44"},{"id":"aitchisonStatisticalAnalysisCompositional1982a","abstract":"The simplex plays an important role as sample space in many practical situations where compositional data, in the form of proportions of some whole, require interpretation. It is argued that the statistical analysis of such data has proved difficult because of a lack both of concepts of independence and of rich enough parametric classes of distributions in the simplex. A variety of independence hypotheses are introduced and interrelated, and new classes of transformed-normal distributions in the simplex are provided as models within which the independence hypotheses can be tested through standard theory of parametric hypothesis testing. The new concepts and statistical methodology are illustrated by a number of applications.","accessed":{"date-parts":[["2025",5,7]]},"author":[{"family":"Aitchison","given":"J."}],"citation-key":"aitchisonStatisticalAnalysisCompositional1982a","container-title":"Journal of the Royal Statistical Society. Series B (Methodological)","ISSN":"0035-9246","issue":"2","issued":{"date-parts":[["1982"]]},"note":"Read_Status: New\nRead_Status_Date: 2025-05-07T07:43:38.485Z","page":"139–177","publisher":"[Royal Statistical Society, Oxford University Press]","source":"JSTOR","title":"The Statistical Analysis of Compositional Data","type":"article-journal","URL":"https://www.jstor.org/stable/2345821","volume":"44"},{"id":"akrawiPurificationSpecificityProlyl1976","abstract":"Prolyl dipeptidase (iminodipeptidase, L-prolyl-amino acid hydrolase, EC 3.4.13.8) was purified 180-fold from bovine kidney. The enzyme which was obtained in a 10% yield was completely separated from a number of known kidney peptidases including an enzyme of very similar substrate specificity, proline aminopeptidase (L-prolyl-peptide hydrolase, EC 3.4.11.5). The specific activity of the enzyme with L-prolylglycine as substrate is 1600 units of activity per mg protein. Optimum activity of the enzyme is at pH 8.75 and the molecular weight on gel filtration was estimated to be 100 000. The isoelectric point of the enzyme is pH 4.25. Studies of substrate specificity showed that the enzyme preferentially hydrolyzes dipeptides and dipeptidyl amides with L-proline or hydroxy-L-proline at the N-terminus. Longer chain substrates with N-terminal proline were not hydrolyzed.","author":[{"family":"Akrawi","given":"A. F."},{"family":"Bailey","given":"G. S."}],"citation-key":"akrawiPurificationSpecificityProlyl1976","container-title":"Biochimica Et Biophysica Acta","container-title-short":"Biochim Biophys Acta","DOI":"10.1016/0005-2744(76)90017-6","ISSN":"0006-3002","issue":"1","issued":{"date-parts":[["1976",1,23]]},"language":"eng","note":"Read_Status: New\nRead_Status_Date: 2025-06-11T15:47:44.999Z","page":"170–178","PMID":"2303","source":"PubMed","title":"Purification and specificity of prolyl dipeptidase from bovine kidney","type":"article-journal","volume":"422"},{"id":"AlphaGo2023","abstract":"AlphaGo est un programme informatique capable de jouer au jeu de go, développé par l'entreprise britannique DeepMind et racheté en 2014 par Google.\nEn octobre 2015, il devient le premier programme à battre un joueur professionnel (le français Fan Hui) sur un goban de taille normale (19×19) sans handicap. Il s'agit d'une étape symboliquement forte puisque le programme joueur de go est alors un défi complexe de l'intelligence artificielle. En mars 2016, il bat Lee Sedol, un des meilleurs joueurs mondiaux (9e dan professionnel),. Le 27 mai 2017, il bat le champion du monde Ke Jie et la retraite du logiciel est annoncée. \nL'algorithme d'AlphaGo combine des techniques d'apprentissage automatique et de parcours de graphe, associées à de nombreux entrainements avec des humains, d'autres ordinateurs, et surtout lui-même. \nCet algorithme sera encore amélioré dans les versions suivantes. AlphaGo Zero en octobre 2017 atteint un niveau supérieur en jouant uniquement contre lui-même. AlphaZero en décembre 2017 surpasse largement, toujours par auto-apprentissage, le niveau de tous les joueurs humains et logiciels, non seulement au go, mais aussi aux échecs et au shōgi.","accessed":{"date-parts":[["2024",2,5]]},"citation-key":"AlphaGo2023","container-title":"Wikipédia","issued":{"date-parts":[["2023",12,10]]},"language":"fr","license":"Creative Commons Attribution-ShareAlike License","note":"Page Version ID: 210417123","source":"Wikipedia","title":"AlphaGo","type":"entry-encyclopedia","URL":"https://fr.wikipedia.org/w/index.php?title=AlphaGo&oldid=210417123"},{"id":"AlphaStarMasteringRealtime2019","abstract":"Games have been used for decades as an important way to test and evaluate the performance of artificial intelligence systems. As capabilities have increased, the research community has sought games with increasing complexity that capture different elements of intelligence required to solve scientific and real-world problems. In recent years, StarCraft, considered to be one of the most challenging Real-Time Strategy (RTS) games and one of the longest-played esports of all time, has emerged by consensus as a “grand challenge” for AI research.","accessed":{"date-parts":[["2024",2,5]]},"citation-key":"AlphaStarMasteringRealtime2019","container-title":"Google DeepMind","issued":{"date-parts":[["2019",1,24]]},"language":"en","title":"AlphaStar: Mastering the real-time strategy game StarCraft II","title-short":"AlphaStar","type":"webpage","URL":"https://deepmind.google/discover/blog/alphastar-mastering-the-real-time-strategy-game-starcraft-ii/"},{"id":"anakokBipartiteGraphVariational2024","abstract":"We propose a method to represent bipartite networks using graph embeddings tailored to tackle the challenges of studying ecological networks, such as the ones linking plants and pollinators, where many covariates need to be accounted for, in particular to control for sampling bias. We adapt the variational graph auto-encoder approach to the bipartite case, which enables us to generate embeddings in a latent space where the two sets of nodes are positioned based on their probability of connection. We translate the fairness framework commonly considered in sociology in order to address sampling bias in ecology. By incorporating the Hilbert-Schmidt independence criterion (HSIC) as an additional penalty term in the loss we optimize, we ensure that the structure of the latent space is independent of continuous variables, which are related to the sampling process. Finally, we show how our approach can change our understanding of ecological networks when applied to the Spipoll data set, a citizen science monitoring program of plant-pollinator interactions to which many observers contribute, making it prone to sampling bias.","accessed":{"date-parts":[["2025",9,21]]},"author":[{"family":"Anakok","given":"Emre"},{"family":"Barbillon","given":"Pierre"},{"family":"Fontaine","given":"Colin"},{"family":"Thebault","given":"Elisa"}],"citation-key":"anakokBipartiteGraphVariational2024","DOI":"10.48550/arXiv.2403.02011","issued":{"date-parts":[["2024",7,15]]},"note":"Read_Status: New\nRead_Status_Date: 2025-09-23T11:03:32.788Z","number":"arXiv:2403.02011","publisher":"arXiv","source":"arXiv.org","title":"Bipartite Graph Variational Auto-Encoder with Fair Latent Representation to Account for Sampling Bias in Ecological Networks","type":"article","URL":"http://arxiv.org/abs/2403.02011"},{"id":"anakokDisentanglingStructureEcological2022","abstract":"The structure of a bipartite interaction network can be described by providing a clustering for each of the two types of nodes. Such clusterings are outputted by fitting a Latent Block Model (LBM) on an observed network that comes from a sampling of species interactions in the field. However, the sampling is limited and possibly uneven. This may jeopardize the fit of the LBM and then the description of the structure of the network by detecting structures which result from the sampling and not from actual underlying ecological phenomena. If the observed interaction network consists of a weighted bipartite network where the number of observed interactions between two species is available, the sampling efforts for all species can be estimated and used to correct the LBM fit. We propose to combine an observation model that accounts for sampling and an LBM for describing the structure of underlying possible ecological interactions. We develop an original inference procedure for this model, the efficiency of which is demonstrated in simulation studies. The practical interest in ecology of our model is highlighted on a large dataset of plant-pollinator network.","accessed":{"date-parts":[["2023",6,14]]},"author":[{"family":"Anakok","given":"Emre"},{"family":"Barbillon","given":"Pierre"},{"family":"Fontaine","given":"Colin"},{"family":"Thebault","given":"Elisa"}],"citation-key":"anakokDisentanglingStructureEcological2022","issued":{"date-parts":[["2022",11,29]]},"language":"en","number":"arXiv:2211.16364","publisher":"arXiv","source":"arXiv.org","title":"Disentangling the structure of ecological bipartite networks from observation processes","type":"article","URL":"http://arxiv.org/abs/2211.16364"},{"id":"andersonNewMethodNonparametric2001","abstract":"Hypothesis-testing methods for multivariate data are needed to make rigorous probability statements about the effects of factors and their interactions in experiments. Analysis of variance is particularly powerful for the analysis of univariate data. The traditional multivariate analogues, however, are too stringent in their assumptions for most ecological multivariate data sets. Non-parametric methods, based on permutation tests, are preferable. This paper describes a new non-parametric method for multivariate analysis of variance, after McArdle and Anderson (in press). It is given here, with several applications in ecology, to provide an alternative and perhaps more intuitive formulation for ANOVA (based on sums of squared distances) to complement the description provided by McArdle and Anderson (in press) for the analysis of any linear model. It is an improvement on previous non-parametric methods because it allows a direct additive partitioning of variation for complex models. It does this while maintaining the flexibility and lack of formal assumptions of other non-parametric methods. The teststatistic is a multivariate analogue to Fisher’s F-ratio and is calculated directly from any symmetric distance or dissimilarity matrix. P-values are then obtained using permutations. Some examples of the method are given for tests involving several factors, including factorial and hierarchical (nested) designs and tests of interactions.","accessed":{"date-parts":[["2025",11,10]]},"author":[{"family":"Anderson","given":"Marti J."}],"citation-key":"andersonNewMethodNonparametric2001","container-title":"Austral Ecology","container-title-short":"Austral Ecology","DOI":"10.1111/j.1442-9993.2001.01070.pp.x","ISSN":"1442-9985, 1442-9993","issue":"1","issued":{"date-parts":[["2001",2]]},"language":"en","note":"Read_Status: New\nRead_Status_Date: 2025-11-10T09:24:58.855Z","page":"32–46","source":"DOI.org (Crossref)","title":"A new method for non‐parametric multivariate analysis of variance","type":"article-journal","URL":"https://onlinelibrary.wiley.com/doi/10.1111/j.1442-9993.2001.01070.pp.x","volume":"26"},{"id":"Appendix1990","accessed":{"date-parts":[["2024",9,13]]},"citation-key":"Appendix1990","container-title":"Finding Groups in Data","DOI":"10.1002/9780470316801.app1","ISBN":"978-0-470-31680-1","issued":{"date-parts":[["1990"]]},"language":"en","page":"312–319","publisher":"John Wiley & Sons, Ltd","source":"Wiley Online Library","title":"Appendix","type":"chapter","URL":"https://onlinelibrary.wiley.com/doi/abs/10.1002/9780470316801.app1"},{"id":"arroyoInferenceMultipleHeterogeneous2021","abstract":"The development of models and methodology for the analysis of data from multiple heterogeneous networks is of importance both in statistical network theory and across a wide spectrum of application domains. Although single-graph analysis is well-studied, multiple graph inference is largely unexplored, in part because of the challenges inherent in appropriately modeling graph differences and yet retaining sufficient model simplicity to render estimation feasible. This paper addresses exactly this gap, by introducing a new model, the common subspace independent-edge multiple random graph model, which describes a heterogeneous collection of networks with a shared latent structure on the vertices but potentially different connectivity patterns for each graph. The model encompasses many popular network representations, including the stochastic blockmodel. The model is both flexible enough to meaningfully account for important graph differences, and tractable enough to allow for accurate inference in multiple networks. In particular, a joint spectral embedding of adjacency matrices---the multiple adjacency spectral embedding---leads to simultaneous consistent estimation of underlying parameters for each graph. Under mild additional assumptions, the estimates satisfy asymptotic normality and yield improvements for graph eigenvalue estimation. In both simulated and real data, the model and the embedding can be deployed for a number of subsequent network inference tasks, including dimensionality reduction, classification, hypothesis testing, and community detection. Specifically, when the embedding is applied to a data set of connectomes constructed through diffusion magnetic resonance imaging, the result is an accurate classification of brain scans by human subject and a meaningful determination of heterogeneity across scans of different individuals.","accessed":{"date-parts":[["2025",9,19]]},"author":[{"family":"Arroyo","given":"Jesús"},{"family":"Athreya","given":"Avanti"},{"family":"Cape","given":"Joshua"},{"family":"Chen","given":"Guodong"},{"family":"Priebe","given":"Carey E."},{"family":"Vogelstein","given":"Joshua T."}],"citation-key":"arroyoInferenceMultipleHeterogeneous2021","container-title":"Journal of Machine Learning Research","ISSN":"1533-7928","issue":"142","issued":{"date-parts":[["2021"]]},"note":"Read_Status: New\nRead_Status_Date: 2025-09-19T14:02:30.452Z","page":"1–49","source":"www.jmlr.org","title":"Inference for Multiple Heterogeneous Networks with a Common Invariant Subspace","type":"article-journal","URL":"http://jmlr.org/papers/v22/19-558.html","volume":"22"},{"id":"arroyoInferenceMultipleHeterogeneous2021","abstract":"The development of models and methodology for the analysis of data from multiple heterogeneous networks is of importance both in statistical network theory and across a wide spectrum of application domains. Although single-graph analysis is well-studied, multiple graph inference is largely unexplored, in part because of the challenges inherent in appropriately modeling graph differences and yet retaining sufficient model simplicity to render estimation feasible. This paper addresses exactly this gap, by introducing a new model, the common subspace independent-edge multiple random graph model, which describes a heterogeneous collection of networks with a shared latent structure on the vertices but potentially different connectivity patterns for each graph. The model encompasses many popular network representations, including the stochastic blockmodel. The model is both flexible enough to meaningfully account for important graph differences, and tractable enough to allow for accurate inference in multiple networks. In particular, a joint spectral embedding of adjacency matrices—the multiple adjacency spectral embedding—leads to simultaneous consistent estimation of underlying parameters for each graph. Under mild additional assumptions, the estimates satisfy asymptotic normality and yield improvements for graph eigenvalue estimation. In both simulated and real data, the model and the embedding can be deployed for a number of subsequent network inference tasks, including dimensionality reduction, classification, hypothesis testing, and community detection. Specifically, when the embedding is applied to a data set of connectomes constructed through diffusion magnetic resonance imaging, the result is an accurate classification of brain scans by human subject and a meaningful determination of heterogeneity across scans of different individuals.","accessed":{"date-parts":[["2025",9,19]]},"author":[{"family":"Arroyo","given":"Jesús"},{"family":"Athreya","given":"Avanti"},{"family":"Cape","given":"Joshua"},{"family":"Chen","given":"Guodong"},{"family":"Priebe","given":"Carey E."},{"family":"Vogelstein","given":"Joshua T."}],"citation-key":"arroyoInferenceMultipleHeterogeneous2021","container-title":"Journal of Machine Learning Research","ISSN":"1533-7928","issue":"142","issued":{"date-parts":[["2021"]]},"page":"1–49","title":"Inference for Multiple Heterogeneous Networks with a Common Invariant Subspace","type":"article-journal","URL":"http://jmlr.org/papers/v22/19-558.html","volume":"22"},{"id":"aubertModelbasedBiclusteringOverdispersed2021","abstract":"Different studies have shown that microbial communities living in animals (humans included), in or around plants have a significant impact on health and disease of their host and on various services, such as adaptation under stressing environment. The basic input data to study microbiomes is a matrix representing abundance data of micro-organisms across different sampling units. Such a matrix typically corresponds to taxonomic profiles derived from the high-throughput sequencing of environmental samples. Biclustering is one way to study the interactions between the structure of micro-organism communities and the environmental samples they come from. We propose a latent block model (LBM) and an associated inference procedure for the biclustering of rows and columns of abundance matrices. The LBM assumes that micro-organisms (rows) and environmental samples (columns) can both be clustered into groups characterizing preferential interaction or avoidance. We use the Poisson–Gamma distribution to model the overdispersion observed in microbial abundance data and introduce row and column effects to account for the sequencing effort in each sample and the mean abundance of each micro-organism. Because the latent variables are not independent conditionally on the observed ones, classical maximum likelihood inference is intractable. We then derive a variational-based inference algorithm and propose a strategy to select the number of biclusters. We illustrate the flexibility and performance of our approach both on a simulation study and on three ecological datasets. The model-based framework allows us to adapt to peculiarities of microbial ecological abundance data and allows us to explore relationships between entities of two different natures. We implemented our method in the cobiclust R package available on the CRAN and built a website with example of usage (https://julieaubert.github.io/cobiclust/cobiclust-example1.html).","accessed":{"date-parts":[["2023",6,22]]},"author":[{"family":"Aubert","given":"Julie"},{"family":"Schbath","given":"Sophie"},{"family":"Robin","given":"Stéphane"}],"citation-key":"aubertModelbasedBiclusteringOverdispersed2021","container-title":"Methods in Ecology and Evolution","DOI":"10.1111/2041-210X.13582","ISSN":"2041-210X","issue":"6","issued":{"date-parts":[["2021"]]},"language":"en","license":"© 2021 British Ecological Society","page":"1050–1061","source":"Wiley Online Library","title":"Model-based biclustering for overdispersed count data with application in microbial ecology","type":"article-journal","URL":"https://onlinelibrary.wiley.com/doi/abs/10.1111/2041-210X.13582","volume":"12"},{"id":"aubertModelbasedBiclusteringOverdispersed2021a","abstract":"Abstract\n \n \n \n Different studies have shown that microbial communities living in animals (humans included), in or around plants have a significant impact on health and disease of their host and on various services, such as adaptation under stressing environment. The basic input data to study microbiomes is a matrix representing abundance data of micro‐organisms across different sampling units. Such a matrix typically corresponds to taxonomic profiles derived from the high‐throughput sequencing of environmental samples. Biclustering is one way to study the interactions between the structure of micro‐organism communities and the environmental samples they come from.\n \n \n We propose a latent block model (LBM) and an associated inference procedure for the biclustering of rows and columns of abundance matrices. The LBM assumes that micro‐organisms (rows) and environmental samples (columns) can both be clustered into groups characterizing preferential interaction or avoidance. We use the Poisson–Gamma distribution to model the overdispersion observed in microbial abundance data and introduce row and column effects to account for the sequencing effort in each sample and the mean abundance of each micro‐organism. Because the latent variables are not independent conditionally on the observed ones, classical maximum likelihood inference is intractable. We then derive a variational‐based inference algorithm and propose a strategy to select the number of biclusters.\n \n \n We illustrate the flexibility and performance of our approach both on a simulation study and on three ecological datasets. The model‐based framework allows us to adapt to peculiarities of microbial ecological abundance data and allows us to explore relationships between entities of two different natures.\n \n \n \n We implemented our method in the\n cobiclust\n R\n package available on the CRAN and built a website with example of usage (\n https://julieaubert.github.io/cobiclust/cobiclust‐example1.html\n ).\n \n \n \n \n , \n Résumé\n \n \n \n Différentes études ont montré que les communautés microbiennes vivant chez les animaux (humains inclus), dans ou autour des plantes ont un impact significatif sur la santé et la maladie de leur hôte et sur divers services, comme l'adaptation dans un environnement stressant. Les données d'entrée pour étudier les microbiomes se représentent sous la forme d'une matrice de données d'abondance des micro‐organismes dans différentes unités d'échantillonnage. Une telle matrice correspond typiquement à des profils taxonomiques issus du séquençage à haut débit d'échantillons environnementaux. La classification croisée (ou biclustering) est un moyen d'étudier les interactions entre la structure des communautés de micro‐organismes et les échantillons environnementaux dont ils proviennent.\n \n \n Nous proposons un modèle de bloc latent (LBM) et une procédure d'inférence associée pour la classification croisée de lignes et de colonnes de matrices d'abondance. Le LBM suppose que les micro‐organismes (lignes) et les échantillons environnementaux (colonnes) peuvent tous deux être regroupés en groupes caractérisant l'interaction préférentielle ou l’évitement. Nous utilisons la distribution Poisson‐Gamma pour modéliser la surdispersion observée dans les données d'abondance microbienne et introduisons des effets ligne et colonne pour tenir compte de l'effort de séquençage dans chaque échantillon et de l'abondance moyenne de chaque microorganisme. Comme les variables latentes ne sont pas conditionnellement indépendantes des variables observées, l'inférence classique du maximum de vraisemblance est insoluble. Nous proposons un algorithme d'inférence variationnel ainsi qu'une stratégie pour sélectionner le nombre de groupes.\n \n \n Nous illustrons la flexibilité et la performance de notre approche à la fois sur une étude de simulation et sur trois jeux de données écologiques. Le cadre basé sur un modèle nous permet de nous adapter aux particularités des données d'abondance en écologie microbienne et nous permet d'explorer les relations entre des entités de deux natures différentes.\n \n \n \n Notre méthode est implémentée dans le package\n R\n cobiclust\n disponible sur le CRAN.","accessed":{"date-parts":[["2025",10,31]]},"author":[{"family":"Aubert","given":"Julie"},{"family":"Schbath","given":"Sophie"},{"family":"Robin","given":"Stéphane"}],"citation-key":"aubertModelbasedBiclusteringOverdispersed2021a","container-title":"Methods in Ecology and Evolution","container-title-short":"Methods Ecol Evol","DOI":"10.1111/2041-210X.13582","ISSN":"2041-210X, 2041-210X","issue":"6","issued":{"date-parts":[["2021",6]]},"language":"en","note":"Read_Status: New\nRead_Status_Date: 2025-10-31T14:41:19.599Z","page":"1050–1061","source":"DOI.org (Crossref)","title":"Model‐based biclustering for overdispersed count data with application in microbial ecology","type":"article-journal","URL":"https://besjournals.onlinelibrary.wiley.com/doi/10.1111/2041-210X.13582","volume":"12"},{"id":"AuthorIndex1990","accessed":{"date-parts":[["2024",9,13]]},"citation-key":"AuthorIndex1990","container-title":"Finding Groups in Data","DOI":"10.1002/9780470316801.indauth","ISBN":"978-0-470-31680-1","issued":{"date-parts":[["1990"]]},"language":"en","page":"322–335","publisher":"John Wiley & Sons, Ltd","source":"Wiley Online Library","title":"Author Index","type":"chapter","URL":"https://onlinelibrary.wiley.com/doi/abs/10.1002/9780470316801.indauth"},{"id":"AutoencodeurVariationnel2024","abstract":"En apprentissage automatique, un auto-encodeur variationnel (ou VAE de l'anglais variational auto encoder), est une architecture de réseau de neurones artificiels introduite en 2013 par D. Kingma et M. Welling, appartenant aux familles des modèles graphiques probabilistes et des méthodes bayésiennes variationnelles.\nLes VAE sont souvent rapprochés des autoencodeurs, en raison de leur architectures similaires. Leur utilisation et leur formulation mathématiques sont cependant différentes.\nLes auto-encodeurs variationnels permettent de formuler un problème d'inférence statistique (par exemple, déduire la valeur d'une variable aléatoire à partir d'une autre variable aléatoire) en un problème d'optimisation statistique (c'est-à-dire trouver les valeurs de paramètres qui minimisent une fonction objectif). Ils représentent une fonction associant à une valeur d'entrée une distribution latente multivariée, qui n'est pas directement observée mais déduite depuis un modèle mathématique à partir de la distribution d'autres variables. Bien que ce type de modèle ait été initialement conçu pour l'apprentissage non supervisé, son efficacité a été prouvée pour l'apprentissage semi-supervisé, et l'apprentissage supervisé.","accessed":{"date-parts":[["2024",5,21]]},"citation-key":"AutoencodeurVariationnel2024","container-title":"Wikipédia","issued":{"date-parts":[["2024",3,13]]},"language":"fr","license":"Creative Commons Attribution-ShareAlike License","note":"Page Version ID: 213326719","source":"Wikipedia","title":"Auto-encodeur variationnel","type":"entry-encyclopedia","URL":"https://fr.wikipedia.org/w/index.php?title=Auto-encodeur_variationnel&oldid=213326719"},{"id":"baldockDailyTemporalStructure2011","abstract":"Ecological interaction networks are a valuable approach to understanding plant–pollinator interactions at the community level. Highly structured daily activity patterns are a feature of the biology of many flower visitors, particularly provisioning female bees, which often visit different floral sources at different times. Such temporal structure implies that presence/absence and relative abundance of specific flower–visitor interactions (links) in interaction networks may be highly sensitive to the daily timing of data collection. Further, relative timing of interactions is central to their possible role in competition or facilitation of seed set among coflowering plants sharing pollinators. To date, however, no study has examined the network impacts of daily temporal variation in visitor activity at a community scale. Here we use temporally structured sampling to examine the consequences of daily activity patterns upon network properties using fully quantified flower–visitor interaction data for a Kenyan savanna habitat. Interactions were sampled at four sequential three-hour time intervals between 06:00 and 18:00, across multiple seasonal time points for two sampling sites. In all data sets the richness and relative abundance of links depended critically on when during the day visitation was observed. Permutation-based null modeling revealed significant temporal structure across daily time intervals at three of the four seasonal time points, driven primarily by patterns in bee activity. This sensitivity of network structure shows the need to consider daily time in network sampling design, both to maximize the probability of sampling links relevant to plant reproductive success and to facilitate appropriate interpretation of interspecific relationships. Our data also suggest that daily structuring at a community level could reduce indirect competitive interactions when coflowering plants share pollinators, as is commonly observed during flowering in highly seasonal habitats.","accessed":{"date-parts":[["2024",7,2]]},"author":[{"family":"Baldock","given":"Katherine C. R."},{"family":"Memmott","given":"Jane"},{"family":"Ruiz-Guajardo","given":"Juan Carlos"},{"family":"Roze","given":"Denis"},{"family":"Stone","given":"Graham N."}],"citation-key":"baldockDailyTemporalStructure2011","container-title":"Ecology","DOI":"10.1890/10-1110.1","ISSN":"1939-9170","issue":"3","issued":{"date-parts":[["2011"]]},"language":"en","license":"© 2011 by the Ecological Society of America","page":"687–698","source":"Wiley Online Library","title":"Daily temporal structure in African savanna flower visitation networks and consequences for network sampling","type":"article-journal","URL":"https://onlinelibrary.wiley.com/doi/abs/10.1890/10-1110.1","volume":"92"},{"id":"baldockDailyTemporalStructure2011","abstract":"Ecological interaction networks are a valuable approach to understanding plant–pollinator interactions at the community level. Highly structured daily activity patterns are a feature of the biology of many flower visitors, particularly provisioning female bees, which often visit different floral sources at different times. Such temporal structure implies that presence/absence and relative abundance of specific flower–visitor interactions (links) in interaction networks may be highly sensitive to the daily timing of data collection. Further, relative timing of interactions is central to their possible role in competition or facilitation of seed set among coflowering plants sharing pollinators. To date, however, no study has examined the network impacts of daily temporal variation in visitor activity at a community scale. Here we use temporally structured sampling to examine the consequences of daily activity patterns upon network properties using fully quantified flower–visitor interaction data for a Kenyan savanna habitat. Interactions were sampled at four sequential three-hour time intervals between 06:00 and 18:00, across multiple seasonal time points for two sampling sites. In all data sets the richness and relative abundance of links depended critically on when during the day visitation was observed. Permutation-based null modeling revealed significant temporal structure across daily time intervals at three of the four seasonal time points, driven primarily by patterns in bee activity. This sensitivity of network structure shows the need to consider daily time in network sampling design, both to maximize the probability of sampling links relevant to plant reproductive success and to facilitate appropriate interpretation of interspecific relationships. Our data also suggest that daily structuring at a community level could reduce indirect competitive interactions when coflowering plants share pollinators, as is commonly observed during flowering in highly seasonal habitats.","accessed":{"date-parts":[["2024",7,2]]},"author":[{"family":"Baldock","given":"Katherine C. R."},{"family":"Memmott","given":"Jane"},{"family":"Ruiz-Guajardo","given":"Juan Carlos"},{"family":"Roze","given":"Denis"},{"family":"Stone","given":"Graham N."}],"citation-key":"baldockDailyTemporalStructure2011","container-title":"Ecology","DOI":"10.1890/10-1110.1","ISSN":"1939-9170","issue":"3","issued":{"date-parts":[["2011"]]},"language":"english","page":"687–698","title":"Daily temporal structure in African savanna flower visitation networks and consequences for network sampling","type":"article-journal","URL":"https://onlinelibrary.wiley.com/doi/abs/10.1890/10-1110.1","volume":"92"},{"id":"baldockSystemsApproachReveals2019","abstract":"Urban areas are often perceived to have lower biodiversity than the wider countryside, but a few small-scale studies suggest that some urban land uses can support substantial pollinator populations. We present a large-scale, well-replicated study of floral resources and pollinators in 360 sites incorporating all major land uses in four British cities. Using a systems approach, we developed Bayesian network models integrating pollinator dispersal and resource switching to estimate city-scale effects of management interventions on plant-pollinator community robustness to species loss. We show that residential gardens and allotments (community gardens) are pollinator 'hotspots': gardens due to their extensive area, and allotments due to their high pollinator diversity and leverage on city-scale plant-pollinator community robustness. Household income was positively associated with pollinator abundance in gardens, highlighting the influence of socioeconomic factors. Our results underpin urban planning recommendations to enhance pollinator conservation, using increasing city-scale community robustness as our measure of success.","author":[{"family":"Baldock","given":"Katherine C. R."},{"family":"Goddard","given":"Mark A."},{"family":"Hicks","given":"Damien M."},{"family":"Kunin","given":"William E."},{"family":"Mitschunas","given":"Nadine"},{"family":"Morse","given":"Helen"},{"family":"Osgathorpe","given":"Lynne M."},{"family":"Potts","given":"Simon G."},{"family":"Robertson","given":"Kirsty M."},{"family":"Scott","given":"Anna V."},{"family":"Staniczenko","given":"Phillip P. A."},{"family":"Stone","given":"Graham N."},{"family":"Vaughan","given":"Ian P."},{"family":"Memmott","given":"Jane"}],"citation-key":"baldockSystemsApproachReveals2019","container-title":"Nature ecology & evolution","container-title-short":"Nat Ecol Evol","DOI":"10.1038/s41559-018-0769-y","ISSN":"2397-334X","issue":"3","issued":{"date-parts":[["2019",3]]},"language":"english","page":"363–373","PMID":"30643247","title":"A systems approach reveals urban pollinator hotspots and conservation opportunities","type":"article-journal","volume":"3"},{"id":"baldockSystemsApproachReveals2019a","abstract":"Urban areas are often perceived to have lower biodiversity than the wider countryside, but a few small-scale studies suggest that some urban land uses can support substantial pollinator populations. We present a large-scale, well-replicated study of floral resources and pollinators in 360 sites incorporating all major land uses in four British cities. Using a systems approach, we developed Bayesian network models integrating pollinator dispersal and resource switching to estimate city-scale effects of management interventions on plant–pollinator community robustness to species loss. We show that residential gardens and allotments (community gardens) are pollinator ‘hotspots’: gardens due to their extensive area, and allotments due to their high pollinator diversity and leverage on city-scale plant–pollinator community robustness. Household income was positively associated with pollinator abundance in gardens, highlighting the influence of socioeconomic factors. Our results underpin urban planning recommendations to enhance pollinator conservation, using increasing city-scale community robustness as our measure of success.","accessed":{"date-parts":[["2024",6,25]]},"author":[{"family":"Baldock","given":"Katherine C. R."},{"family":"Goddard","given":"Mark A."},{"family":"Hicks","given":"Damien M."},{"family":"Kunin","given":"William E."},{"family":"Mitschunas","given":"Nadine"},{"family":"Morse","given":"Helen"},{"family":"Osgathorpe","given":"Lynne M."},{"family":"Potts","given":"Simon G."},{"family":"Robertson","given":"Kirsty M."},{"family":"Scott","given":"Anna V."},{"family":"Staniczenko","given":"Phillip P. A."},{"family":"Stone","given":"Graham N."},{"family":"Vaughan","given":"Ian P."},{"family":"Memmott","given":"Jane"}],"citation-key":"baldockSystemsApproachReveals2019a","container-title":"Nature Ecology & Evolution","container-title-short":"Nat Ecol Evol","DOI":"10.1038/s41559-018-0769-y","ISSN":"2397-334X","issue":"3","issued":{"date-parts":[["2019",3]]},"language":"en","license":"2019 The Author(s), under exclusive licence to Springer Nature Limited","page":"363–373","publisher":"Nature Publishing Group","source":"www.nature.com","title":"A systems approach reveals urban pollinator hotspots and conservation opportunities","type":"article-journal","URL":"https://www.nature.com/articles/s41559-018-0769-y","volume":"3"},{"id":"baldockSystemsApproachReveals2019b","abstract":"Urban areas are often perceived to have lower biodiversity than the wider countryside, but a few small-scale studies suggest that some urban land uses can support substantial pollinator populations. We present a large-scale, well-replicated study of floral resources and pollinators in 360 sites incorporating all major land uses in four British cities. Using a systems approach, we developed Bayesian network models integrating pollinator dispersal and resource switching to estimate city-scale effects of management interventions on plant-pollinator community robustness to species loss. We show that residential gardens and allotments (community gardens) are pollinator 'hotspots': gardens due to their extensive area, and allotments due to their high pollinator diversity and leverage on city-scale plant-pollinator community robustness. Household income was positively associated with pollinator abundance in gardens, highlighting the influence of socioeconomic factors. Our results underpin urban planning recommendations to enhance pollinator conservation, using increasing city-scale community robustness as our measure of success.","author":[{"family":"Baldock","given":"Katherine C. R."},{"family":"Goddard","given":"Mark A."},{"family":"Hicks","given":"Damien M."},{"family":"Kunin","given":"William E."},{"family":"Mitschunas","given":"Nadine"},{"family":"Morse","given":"Helen"},{"family":"Osgathorpe","given":"Lynne M."},{"family":"Potts","given":"Simon G."},{"family":"Robertson","given":"Kirsty M."},{"family":"Scott","given":"Anna V."},{"family":"Staniczenko","given":"Phillip P. A."},{"family":"Stone","given":"Graham N."},{"family":"Vaughan","given":"Ian P."},{"family":"Memmott","given":"Jane"}],"citation-key":"baldockSystemsApproachReveals2019b","container-title":"Nature Ecology & Evolution","container-title-short":"Nat Ecol Evol","DOI":"10.1038/s41559-018-0769-y","ISSN":"2397-334X","issue":"3","issued":{"date-parts":[["2019",3]]},"language":"eng","page":"363–373","PMCID":"PMC6445365","PMID":"30643247","source":"PubMed","title":"A systems approach reveals urban pollinator hotspots and conservation opportunities","type":"article-journal","volume":"3"},{"id":"barberoSabinaHSBMPackageLink","author":[{"family":"Barbero","given":"Jennifer Morales"}],"citation-key":"barberoSabinaHSBMPackageLink","language":"en","note":"Read_Status: New\nRead_Status_Date: 2025-11-03T12:31:15.098Z","source":"Zotero","title":"sabinaHSBM: An R package for link prediction network reconstruction using Hierarchical Stochastic Block Models","type":"article-journal"},{"id":"barbillonCoursTheorieMesure","author":[{"family":"Barbillon","given":"Pierre"}],"citation-key":"barbillonCoursTheorieMesure","title":"Cours Théorie de la mesure APT 2A","type":"document"},{"id":"barbillonSciencesDonneesApprentissage","author":[{"family":"Barbillon","given":"Pierre"}],"citation-key":"barbillonSciencesDonneesApprentissage","language":"fr","source":"Zotero","title":"Sciences des données : apprentissage statistique","type":"article-journal"},{"id":"barbillonSciencesDonneesApprentissagea","author":[{"family":"Barbillon","given":"Pierre"}],"citation-key":"barbillonSciencesDonneesApprentissagea","language":"fr","note":"Read_Status: New\nRead_Status_Date: 2025-10-23T12:22:59.911Z","source":"Zotero","title":"Sciences des données : apprentissage statistique","type":"article-journal"},{"id":"bartlettPropertiesSufficiencyStatistical1997","abstract":"1—In a previous paper, dealing with the importance of properties of sufficiency in the statistical theory of small samples, attention was mainly confined to the theory of estimation. In the present paper the structure of small sample tests, whether these are related to problems of estimation and fiducial distributions, or are of the nature of tests of goodness of fit, is considered further. The notation a | b implies as before that the variate a is conditioned by a given value of b. The fixed variate b may be denoted by | b, and analogously if b is clear from the context, a | b may be written simply as a |. Corresponding to the idea of ancillary information introduced by Fisher for the case of a single unknown θ, where auxiliary statistics control the accuracy of our estimate, I have termed a conditional statistic of the form T |, quasi-sufficient, if its distribution satisfies the “sufficiency” property and contains all the information on θ. In the more general case of other unknowns, such a statistic may contain all the available information on θ.","accessed":{"date-parts":[["2024",3,17]]},"author":[{"family":"Bartlett","given":"Maurice Stevenson"},{"family":"Fowler","given":"Ralph Howard"}],"citation-key":"bartlettPropertiesSufficiencyStatistical1997","container-title":"Proceedings of the Royal Society of London. Series A - Mathematical and Physical Sciences","DOI":"10.1098/rspa.1937.0109","issue":"901","issued":{"date-parts":[["1997",1]]},"page":"268–282","publisher":"Royal Society","source":"royalsocietypublishing.org (Atypon)","title":"Properties of sufficiency and statistical tests","type":"article-journal","URL":"https://royalsocietypublishing.org/doi/10.1098/rspa.1937.0109","volume":"160"},{"id":"bashanUniversalityHumanMicrobial2016","abstract":"The recent realization that human-associated microbial communities play a crucial role in determining our health and well-being, has led to the ongoing development of microbiome-based therapies such as fecal microbiota transplantation,. Thosemicrobial communities are very complex, dynamic and highly personalized ecosystems,, exhibiting a high degree of inter-individual variability in both species assemblages and abundance profiles. It is not known whether the underlying ecological dynamics, which can be parameterized by growth rates, intra- and inter-species interactions in population dynamics models, are largely host-independent (i.e. “universal”) or host-specific. If the inter-individual variability reflects host-specific dynamics due to differences in host lifestyle, physiology, or genetics, then generic microbiome manipulations may have unintended consequences, rendering them ineffectual or even detrimental. Alternatively, microbial ecosystems of different subjects may follow a universal dynamics with the inter-individual variability mainly stemming from differences in the sets of colonizing species,. Here we developed a novel computational method to characterize human microbial dynamics. Applying this method to cross-sectional data from two large-scale metagenomic studies, the Human Microbiome Project, and the Student Microbiome Project, we found that both gut and mouth microbiomes display pronounced universal dynamics, whereas communities associated with certain skin sites are likely shaped by differences in the host environment. Interestingly, the universality of gut microbial dynamics is not observed in subjects with recurrent Clostridium difficile infection but is observed in the same set of subjects after fecal microbiota transplantation. These results fundamentally improve our understanding of forces and processes shaping human microbial ecosystems, paving the way to design general microbiome-based therapies.","accessed":{"date-parts":[["2025",5,5]]},"author":[{"family":"Bashan","given":"Amir"},{"family":"Gibson","given":"Travis E."},{"family":"Friedman","given":"Jonathan"},{"family":"Carey","given":"Vincent J."},{"family":"Weiss","given":"Scott T."},{"family":"Hohmann","given":"Elizabeth L."},{"family":"Liu","given":"Yang-Yu"}],"citation-key":"bashanUniversalityHumanMicrobial2016","container-title":"Nature","container-title-short":"Nature","DOI":"10.1038/nature18301","ISSN":"0028-0836","issue":"7606","issued":{"date-parts":[["2016",6,8]]},"note":"Read_Status: New\nRead_Status_Date: 2025-05-05T15:33:24.405Z","page":"259–262","PMCID":"PMC4902290","PMID":"27279224","source":"PubMed Central","title":"Universality of Human Microbial Dynamics","type":"article-journal","URL":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4902290/","volume":"534"},{"id":"bastideContinuousTraitEvolution2022","author":[{"family":"Bastide","given":"Paul"},{"family":"Clavel","given":"Julien"}],"citation-key":"bastideContinuousTraitEvolution2022","issued":{"date-parts":[["2022",12]]},"language":"en","title":"Continuous Trait Evolution","type":"speech"},{"id":"bastideModelesDevolutionCaracteres2022","abstract":"On s'intéresse ici à la probabilité de passer d'un état A à un état B mais dans le cas où le caractère d'intérêt est un trait quantitatif comme la taille ou le poids. Les modèles utilisés diffèrent du cas discrets et dérivent principalement du mouvement brownien. Ce chapitre présente les principaux modèles d'évolution de traits quantitatifs ainsi que les méthodes permettant de les appliquer dans le contexte évolutif.","accessed":{"date-parts":[["2023",11,14]]},"author":[{"family":"Bastide","given":"Paul"},{"family":"Mariadassou","given":"Mahendra"},{"family":"Robin","given":"Stéphane"}],"citation-key":"bastideModelesDevolutionCaracteres2022","container-author":[{"family":"Didier","given":"Gilles"},{"family":"Guindon","given":"Stéphane"}],"container-title":"Modèles et méthodes pour l’évolution biologique","DOI":"10.51926/ISTE.9069.ch3","ISBN":"978-1-78948-069-6","issued":{"date-parts":[["2022",7]]},"language":"fr","page":"47–85","publisher":"ISTE Group","source":"DOI.org (Crossref)","title":"Modèles d’évolution de caractères continus","type":"chapter","URL":"https://www.istegroup.com/fr/produit/modeles-et-methodes-pour-levolution-biologique/?/47495"},{"id":"bastidePhylogeneticFrameworkSimulate2023","abstract":"Interspecies RNA-Seq datasets are increasingly common, and have the potential to answer new questions about the evolution of gene expression. Single-species differential expression analysis is now a well-studied problem that benefits from sound statistical methods. Extensive reviews on biological or synthetic datasets have provided the community with a clear picture on the relative performances of the available methods in various settings. However, synthetic dataset simulation tools are still missing in the interspecies gene expression context. In this work, we develop and implement a new simulation framework. This tool builds on both the RNA-Seq and the phylogenetic comparative methods literatures to generate realistic count datasets, while taking into account the phylogenetic relationships between the samples. We illustrate the usefulness of this new framework through a targeted simulation study, that reproduces the features of a recently published dataset, containing gene expression data in adult eye tissue across blind and sighted freshwater crayfish species. Using our simulated datasets, we perform a fair comparison of several approaches used for differential expression analysis. This benchmark reveals some of the strengths and weaknesses of both the classical and phylogenetic approaches for interspecies differential expression analysis, and allows for a reanalysis of the crayfish dataset. The tool has been integrated in the R package compcodeR, freely available on Bioconductor.","accessed":{"date-parts":[["2023",11,20]]},"author":[{"family":"Bastide","given":"Paul"},{"family":"Soneson","given":"Charlotte"},{"family":"Stern","given":"David B"},{"family":"Lespinet","given":"Olivier"},{"family":"Gallopin","given":"Mélina"}],"citation-key":"bastidePhylogeneticFrameworkSimulate2023","container-title":"Molecular Biology and Evolution","container-title-short":"Molecular Biology and Evolution","DOI":"10.1093/molbev/msac269","ISSN":"1537-1719","issue":"1","issued":{"date-parts":[["2023",1,1]]},"page":"msac269","source":"Silverchair","title":"A Phylogenetic Framework to Simulate Synthetic Interspecies RNA-Seq Data","type":"article-journal","URL":"https://doi.org/10.1093/molbev/msac269","volume":"40"},{"id":"battagliaRelationalInductiveBiases2018","abstract":"Artificial intelligence (AI) has undergone a renaissance recently, making major progress in key domains such as vision, language, control, and decision-making. This has been due, in part, to cheap data and cheap compute resources, which have fit the natural strengths of deep learning. However, many defining characteristics of human intelligence, which developed under much different pressures, remain out of reach for current approaches. In particular, generalizing beyond one's experiences--a hallmark of human intelligence from infancy--remains a formidable challenge for modern AI. The following is part position paper, part review, and part unification. We argue that combinatorial generalization must be a top priority for AI to achieve human-like abilities, and that structured representations and computations are key to realizing this objective. Just as biology uses nature and nurture cooperatively, we reject the false choice between \"hand-engineering\" and \"end-to-end\" learning, and instead advocate for an approach which benefits from their complementary strengths. We explore how using relational inductive biases within deep learning architectures can facilitate learning about entities, relations, and rules for composing them. We present a new building block for the AI toolkit with a strong relational inductive bias--the graph network--which generalizes and extends various approaches for neural networks that operate on graphs, and provides a straightforward interface for manipulating structured knowledge and producing structured behaviors. We discuss how graph networks can support relational reasoning and combinatorial generalization, laying the foundation for more sophisticated, interpretable, and flexible patterns of reasoning. As a companion to this paper, we have released an open-source software library for building graph networks, with demonstrations of how to use them in practice.","accessed":{"date-parts":[["2024",5,15]]},"author":[{"family":"Battaglia","given":"Peter W."},{"family":"Hamrick","given":"Jessica B."},{"family":"Bapst","given":"Victor"},{"family":"Sanchez-Gonzalez","given":"Alvaro"},{"family":"Zambaldi","given":"Vinicius"},{"family":"Malinowski","given":"Mateusz"},{"family":"Tacchetti","given":"Andrea"},{"family":"Raposo","given":"David"},{"family":"Santoro","given":"Adam"},{"family":"Faulkner","given":"Ryan"},{"family":"Gulcehre","given":"Caglar"},{"family":"Song","given":"Francis"},{"family":"Ballard","given":"Andrew"},{"family":"Gilmer","given":"Justin"},{"family":"Dahl","given":"George"},{"family":"Vaswani","given":"Ashish"},{"family":"Allen","given":"Kelsey"},{"family":"Nash","given":"Charles"},{"family":"Langston","given":"Victoria"},{"family":"Dyer","given":"Chris"},{"family":"Heess","given":"Nicolas"},{"family":"Wierstra","given":"Daan"},{"family":"Kohli","given":"Pushmeet"},{"family":"Botvinick","given":"Matt"},{"family":"Vinyals","given":"Oriol"},{"family":"Li","given":"Yujia"},{"family":"Pascanu","given":"Razvan"}],"citation-key":"battagliaRelationalInductiveBiases2018","DOI":"10.48550/arXiv.1806.01261","issued":{"date-parts":[["2018",10,17]]},"number":"arXiv:1806.01261","publisher":"arXiv","source":"arXiv.org","title":"Relational inductive biases, deep learning, and graph networks","type":"article","URL":"http://arxiv.org/abs/1806.01261"},{"id":"battistonHierarchicalStochasticBlock2024","abstract":"In many research fields, there is an increased availability of network data arising as multiple networks. However, most statistical models for network data in the literature are designed for a single network. Among these, the Stochastic Block Model is arguably the most popular model to perform vertex clustering and community detection. We propose the Hierarchical Stochastic Block Model, a generalization of the SBM to the setting of multiple networks. This model uses a Hierarchical Pitman-Yor prior for the block allocation vector of each graph. The proposed model has two main advantages: 1) it allows different networks to share the same latent blocks and the level of sharing is learnt from the data; 2) the number of blocks in each graph and the overall number of blocks are learnt from the data too, hence avoiding complicated model selection procedures. We derive both MCMC and Variational Inference algorithms. The former targets the correct posterior and is tuning-free, while the latter relies on an approximation of the posterior distribution, but is potentially more scalable than MCMC. We apply the HSBM to a co-authorship network and a brain connectomic network, to illustrate how the model is able to capture different levels of block sharing.","accessed":{"date-parts":[["2024",7,8]]},"author":[{"family":"Battiston","given":"Marco"},{"family":"Lee","given":"Clement"}],"citation-key":"battistonHierarchicalStochasticBlock2024","DOI":"10.21203/rs.3.rs-4601684/v1","issued":{"date-parts":[["2024",7,4]]},"language":"en","license":"https://creativecommons.org/licenses/by/4.0/","source":"In Review","title":"The Hierarchical Stochastic Block Model for Multiple Networks","type":"article","URL":"https://www.researchsquare.com/article/rs-4601684/v1"},{"id":"beauguitteLanalyseGraphesBipartis","author":[{"family":"Beauguitte","given":"Laurent"}],"citation-key":"beauguitteLanalyseGraphesBipartis","language":"fr","source":"Zotero","title":"L'analyse des graphes bipartis","type":"article-journal"},{"id":"beaumontApproximateBayesianComputation2010","abstract":"In the past 10 years a statistical technique, approximate Bayesian computation (ABC), has been developed that can be used to infer parameters and choose between models in the complicated scenarios that are often considered in the environmental sciences. For example, based on gene sequence and microsatellite data, the method has been used to choose between competing models of human demographic history as well as to infer growth rates, times of divergence, and other parameters. The method fits naturally in the Bayesian inferential framework, and a brief overview is given of the key concepts. Three main approaches to ABC have been developed, and these are described and compared. Although the method arose in population genetics, ABC is increasingly used in other fields, including epidemiology, systems biology, ecology, and agent-based modeling, and many of these applications are briefly described.","accessed":{"date-parts":[["2026",5,13]]},"author":[{"family":"Beaumont","given":"Mark A."}],"citation-key":"beaumontApproximateBayesianComputation2010","container-title":"Annual Review of Ecology, Evolution, and Systematics","container-title-short":"Annu. Rev. Ecol. Evol. Syst.","DOI":"10.1146/annurev-ecolsys-102209-144621","ISSN":"1543-592X, 1545-2069","issue":"1","issued":{"date-parts":[["2010",12,1]]},"language":"en","note":"Read_Status: New\nRead_Status_Date: 2026-05-13T14:01:45.947Z","page":"379–406","source":"DOI.org (Crossref)","title":"Approximate Bayesian Computation in Evolution and Ecology","type":"article-journal","URL":"https://www.annualreviews.org/doi/10.1146/annurev-ecolsys-102209-144621","volume":"41"},{"id":"becoche-mosqueraUnravelingPlantpollinatorInteractions2023","abstract":"Background Loss of biological connectivity increases the vulnerability of ecological dynamics, thereby affecting processes such as pollination. Therefore, it is important to understand the roles of the actors that participate in these interaction networks. Nonetheless, there is a significant oversight regarding the main actors in the pollination networks within the highly biodiverse forests of Colombia. Hence, the present study aims to evaluate the interaction patterns of a network of potential pollinators that inhabit an Andean Forest in Totoró, Cauca, Colombia. Methods The interactions between plants and potential pollinators were recorded through direct observation in 10 transects during six field trips conducted over the course of one year. Subsequently, an interaction matrix was developed, and network metrics such as connectance, specialization, nestedness, and asymmetry of interaction strength were evaluated by applying null models. An interpolation/extrapolation curve was calculated in order to assess the representativeness of the sample. Finally, the key species of the network were identified by considering degree (k), centrality, and betweenness centrality. Results A total of 53 plant species and 52 potential pollinator species (including insects and birds) were recorded, with a sample coverage of 88.5%. Connectance (C = 0.19) and specialization (H2’ = 0.19) were low, indicating a generalist network. Freziera canescens, Gaiadendron punctatum, Persea mutisii, Bombus rubicundus, Heliangelus exortis, Chironomus sp., and Metallura tyrianthina were identified as the key species that contribute to a more cohesive network structure. Discussion The present study characterized the structure of the plant-pollinator network in a highly diverse Andean forest in Colombia. It is evident that insects are the largest group of pollinators; however, it is interesting to note that birds form a different module that specializes in pollinating a specific group of plants. On the other hand, the diversity and generality of the species found suggest that the network may be robust against chains of extinction. Nevertheless, the presence of certain introduced species, such as Apis mellifera, and the rapid changes in vegetation cover may affect the dynamics of this mutualistic network. So, it is imperative to apply restoration and conservation strategies to these ecosystems in order to enhance plant-animal interactions and prevent the loss of taxonomical and functional diversity.","accessed":{"date-parts":[["2025",9,21]]},"author":[{"family":"Becoche-Mosquera","given":"Jorge Mario"},{"family":"Gomez-Bernal","given":"Luis German"},{"family":"Zambrano-Gonzalez","given":"Giselle"},{"family":"Angulo-Ortiz","given":"David"}],"citation-key":"becoche-mosqueraUnravelingPlantpollinatorInteractions2023","container-title":"PeerJ","container-title-short":"PeerJ","DOI":"10.7717/peerj.16133","ISSN":"2167-8359","issued":{"date-parts":[["2023",11,9]]},"language":"en","note":"Read_Status: New\nRead_Status_Date: 2025-09-23T11:03:33.322Z","page":"e16133","publisher":"PeerJ Inc.","source":"peerj.com","title":"Unraveling plant-pollinator interactions from a south-west Andean forest in Colombia","type":"article-journal","URL":"https://peerj.com/articles/16133","volume":"11"},{"id":"belModeleLineaireSes","author":[{"family":"Bel","given":"L"},{"family":"Daudin","given":"JJ"},{"family":"Etienne","given":"M"},{"family":"Lebarbier","given":"E"},{"family":"Mary-Huard","given":"T"},{"family":"Robin","given":"S"},{"family":"Vuillet","given":"C"}],"citation-key":"belModeleLineaireSes","language":"fr","source":"Zotero","title":"Le Modèle Linéaire et ses Extensions","type":"book"},{"id":"benjaminiControllingFalseDiscovery1995","abstract":"The common approach to the multiplicity problem calls for controlling the familywise error rate (FWER). This approach, though, has faults, and we point out a few. A different approach to problems of multiple significance testing is presented. It calls for controlling the expected proportion of falsely rejected hypotheses-the false discovery rate. This error rate is equivalent to the FWER when all hypotheses are true but is smaller otherwise. Therefore, in problems where the control of the false discovery rate rather than that of the FWER is desired, there is potential for a gain in power. A simple sequential Bonferroni-type procedure is proved to control the false discovery rate for independent test statistics, and a simulation study shows that the gain in power is substantial. The use of the new procedure and the appropriateness of the criterion are illustrated with examples.","accessed":{"date-parts":[["2024",3,17]]},"author":[{"family":"Benjamini","given":"Yoav"},{"family":"Hochberg","given":"Yosef"}],"citation-key":"benjaminiControllingFalseDiscovery1995","container-title":"Journal of the Royal Statistical Society. Series B (Methodological)","ISSN":"0035-9246","issue":"1","issued":{"date-parts":[["1995"]]},"page":"289–300","publisher":"[Royal Statistical Society, Wiley]","source":"JSTOR","title":"Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing","title-short":"Controlling the False Discovery Rate","type":"article-journal","URL":"https://www.jstor.org/stable/2346101","volume":"57"},{"id":"berkmenChestRoentgenographyWindow1975","abstract":"The chest roentgenographic findings in Takayasu's arteritis include widening of the ascending aorta, contour irregularities of the descending aorta, arotic calcifications, pulmonary arterial changes, rib notching, and hilar lymphadenopathy. The single most important diagnostic sign is a segmental calcification outlining a localized or diffuse narrowing of the aorta. The other signs may be suspicious or suggestive, but the diagnostic accuracy increases when several findings are present simultaneously.","author":[{"family":"Berkmen","given":"Y. M."},{"family":"Lande","given":"A."}],"citation-key":"berkmenChestRoentgenographyWindow1975","container-title":"The American Journal of Roentgenology, Radium Therapy, and Nuclear Medicine","container-title-short":"Am J Roentgenol Radium Ther Nucl Med","DOI":"10.2214/ajr.125.4.842","ISSN":"0002-9580","issue":"4","issued":{"date-parts":[["1975",12]]},"language":"eng","note":"Read_Status: New\nRead_Status_Date: 2025-06-11T15:47:30.874Z","page":"842–846","PMID":"2023","source":"PubMed","title":"Chest roentgenography as a window to the diagnosis of Takayasu's arteritis","type":"article-journal","volume":"125"},{"id":"berkmenChestRoentgenographyWindow1975a","abstract":"The chest roentgenographic findings in Takayasu's arteritis include widening of the ascending aorta, contour irregularities of the descending aorta, arotic calcifications, pulmonary arterial changes, rib notching, and hilar lymphadenopathy. The single most important diagnostic sign is a segmental calcification outlining a localized or diffuse narrowing of the aorta. The other signs may be suspicious or suggestive, but the diagnostic accuracy increases when several findings are present simultaneously.","author":[{"family":"Berkmen","given":"Y. M."},{"family":"Lande","given":"A."}],"citation-key":"berkmenChestRoentgenographyWindow1975a","container-title":"The American Journal of Roentgenology, Radium Therapy, and Nuclear Medicine","container-title-short":"Am J Roentgenol Radium Ther Nucl Med","DOI":"10.2214/ajr.125.4.842","ISSN":"0002-9580","issue":"4","issued":{"date-parts":[["1975",12]]},"language":"eng","note":"Read_Status: New\nRead_Status_Date: 2025-06-11T15:47:44.999Z","page":"842–846","PMID":"2023","source":"PubMed","title":"Chest roentgenography as a window to the diagnosis of Takayasu's arteritis","type":"article-journal","volume":"125"},{"id":"BgeeGeneExpression","abstract":"Bgee is a database for retrieval and comparison of gene expression patterns across multiple animal species. It provides an intuitive answer to the question -where is a gene expressed?- and supports research in cancer and agriculture as well as evolutionary biology.","accessed":{"date-parts":[["2023",11,20]]},"citation-key":"BgeeGeneExpression","language":"en","license":"Bgee copyright 2007/2023 SIB/UNIL","title":"Bgee: gene expression data in animals","title-short":"Bgee","type":"webpage","URL":"https://www.bgee.org/"},{"id":"bickelNonparametricViewNetwork2009","abstract":"Prompted by the increasing interest in networks in many fields, we present an attempt at unifying points of view and analyses of these objects coming from the social sciences, statistics, probability and physics communities. We apply our approach to the Newman–Girvan modularity, widely used for “community” detection, among others. Our analysis is asymptotic but we show by simulation and application to real examples that the theory is a reasonable guide to practice.","accessed":{"date-parts":[["2024",11,22]]},"author":[{"family":"Bickel","given":"Peter J."},{"family":"Chen","given":"Aiyou"}],"citation-key":"bickelNonparametricViewNetwork2009","container-title":"Proceedings of the National Academy of Sciences","container-title-short":"Proc. Natl. Acad. Sci. U.S.A.","DOI":"10.1073/pnas.0907096106","ISSN":"0027-8424, 1091-6490","issue":"50","issued":{"date-parts":[["2009",12,15]]},"language":"en","page":"21068–21073","source":"DOI.org (Crossref)","title":"A nonparametric view of network models and Newman–Girvan and other modularities","type":"article-journal","URL":"https://pnas.org/doi/full/10.1073/pnas.0907096106","volume":"106"},{"id":"biernackiAssessingMixtureModel2000","abstract":"We propose an assessing method of mixture model in a cluster analysis setting with integrated completed likelihood. For this purpose, the observed data are assigned to unknown clusters using a maximum a posteriori operator. Then, the integrated completed likelihood (ICL) is approximated using the Bayesian information criterion (BIC). Numerical experiments on simulated and real data of the resulting ICL criterion show that it performs well both for choosing a mixture model and a relevant number of clusters. In particular, ICL appears to be more robust than BIC to violation of some of the mixture model assumptions and it can select a number of dusters leading to a sensible partitioning of the data.","author":[{"family":"Biernacki","given":"C."},{"family":"Celeux","given":"G."},{"family":"Govaert","given":"G."}],"citation-key":"biernackiAssessingMixtureModel2000","container-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence","DOI":"10.1109/34.865189","event-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence","ISSN":"1939-3539","issue":"7","issued":{"date-parts":[["2000",7]]},"page":"719–725","source":"IEEE Xplore","title":"Assessing a mixture model for clustering with the integrated completed likelihood","type":"article-journal","volume":"22"},{"id":"biernackiAssessingMixtureModel2000","abstract":"We propose an assessing method of mixture model in a cluster analysis setting with integrated completed likelihood. For this purpose, the observed data are assigned to unknown clusters using a maximum a posteriori operator. Then, the integrated completed likelihood (ICL) is approximated using the Bayesian information criterion (BIC). Numerical experiments on simulated and real data of the resulting ICL criterion show that it performs well both for choosing a mixture model and a relevant number of clusters. In particular, ICL appears to be more robust than BIC to violation of some of the mixture model assumptions and it can select a number of dusters leading to a sensible partitioning of the data.","author":[{"family":"Biernacki","given":"C."},{"family":"Celeux","given":"G."},{"family":"Govaert","given":"G."}],"citation-key":"biernackiAssessingMixtureModel2000","container-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence","DOI":"10.1109/34.865189","ISSN":"1939-3539","issue":"7","issued":{"date-parts":[["2000",7]]},"page":"719–725","title":"Assessing a mixture model for clustering with the integrated completed likelihood","type":"article-journal","volume":"22"},{"id":"blondelAitchisonGeometrySimplex2026","abstract":"Most algorithms for hyperspectral image unmixing produce point estimates of fractional abundances of the materials to be separated. However, in the absence of reliable ground truth, the ability to perform abundance uncertainty quantification (UQ) should be an important feature of algorithms, e.g. to evaluate how hard the unmixing problem is and how much the results should be trusted. The usual modeling assumptions in Bayesian unmixing rely heavily on the Euclidean geometry of the simplex and typically disregard spatial information. In addition, to our knowledge, abundance UQ is close to nonexistent in the literature. In this paper, we propose to leverage Aitchison geometry used in compositional data analysis to provide practitioners with alternative tools for modeling prior abundance distributions. In particular, we show how to design simplex-valued Gaussian Process priors using this geometry. Then we link Aitchison geometry to constrained optimization and sampling algorithms, and propose UQ diagnostics that comply with the constraints on abundance vectors. We illustrate these concepts on real and simulated data.","accessed":{"date-parts":[["2026",4,12]]},"author":[{"family":"Blondel","given":"Hector"},{"family":"Drumetz","given":"Lucas"},{"family":"Chonavel","given":"Thierry"}],"citation-key":"blondelAitchisonGeometrySimplex2026","DOI":"10.48550/arXiv.2603.24108","issued":{"date-parts":[["2026",3,25]]},"language":"en","note":"Read_Status: New\nRead_Status_Date: 2026-04-12T17:36:21.854Z","number":"arXiv:2603.24108","publisher":"arXiv","source":"arXiv.org","title":"Aitchison Geometry on the Simplex for Uncertainty Quantification in Bayesian Hyperspectral Image Unmixing","type":"article","URL":"http://arxiv.org/abs/2603.24108"},{"id":"boschPlantPollinatorNetworks2009","abstract":"Pollination network studies are based on pollinator surveys conducted on focal plants. This plant-centred approach provides insufficient information on flower visitation habits of rare pollinator species, which are the majority in pollinator communities. As a result, pollination networks contain very high proportions of pollinator species linked to a single plant species (extreme specialists), a pattern that contrasts with the widely accepted view that plant–pollinator interactions are mostly generalized. In this study of a Mediterranean scrubland community in NE Spain we supplement data from an intensive field survey with the analysis of pollen loads carried by pollinators. We observed 4265 contacts corresponding to 19 plant and 122 pollinator species. The addition of pollen data unveiled a very significant number of interactions, resulting in important network structural changes. Connectance increased 1.43-fold, mean plant connectivity went from 18.5 to 26.4, and mean pollinator connectivity from 2.9 to 4.1. Extreme specialist pollinator species decreased 0.6-fold, suggesting that ecological specialization is often overestimated in plant–pollinator networks. We expected a greater connectivity increase in rare species, and consequently a decrease in the level of asymmetric specialization. However, new links preferentially attached to already highly connected nodes and, as a result, both nestedness and centralization increased. The addition of pollen data revealed the existence of four clearly defined modules that were not apparent when only field survey data were used. Three of these modules had a strong phenological component. In comparison to other pollination webs, our network had a high proportion of connector links and species. That is, although significant, the four modules were far from isolated.","accessed":{"date-parts":[["2024",8,20]]},"author":[{"family":"Bosch","given":"Jordi"},{"family":"Martín González","given":"Ana M."},{"family":"Rodrigo","given":"Anselm"},{"family":"Navarro","given":"David"}],"citation-key":"boschPlantPollinatorNetworks2009","container-title":"Ecology Letters","DOI":"10.1111/j.1461-0248.2009.01296.x","ISSN":"1461-0248","issue":"5","issued":{"date-parts":[["2009"]]},"language":"en","license":"© 2009 Blackwell Publishing Ltd/CNRS","page":"409–419","source":"Wiley Online Library","title":"Plant–pollinator networks: adding the pollinator’s perspective","title-short":"Plant–pollinator networks","type":"article-journal","URL":"https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1461-0248.2009.01296.x","volume":"12"},{"id":"botellaAppraisalGraphEmbeddings2022","abstract":"Comparing the architecture of interaction networks in space or time is essential for understanding the assembly, trajectory, functioning and persistence of species communities. Graph embedding methods, which position networks into a vector space where nearby networks have similar architectures, could be ideal tools for this purposes. Here, we evaluated the ability of seven graph embedding methods to disentangle architectural similarities of interactions networks for supervised and unsupervised posterior analytic tasks. The evaluation was carried out over a large number of simulated trophic networks representing variations around six ecological properties and size. We did not find an overall best method and instead showed that the performance of the methods depended on the targeted ecological properties and thus on the research questions. We also highlighted the importance of normalising the embedding for network sizes for meaningful posterior unsupervised analyses. We concluded by orientating potential users to the most suited methods given the question, the targeted network ecological property, and outlined links between those ecological properties and three ecological processes: robustness to extinction, community persistence and ecosystem functioning. We hope this study will stimulate the appropriation of graph embedding methods by ecologists.","accessed":{"date-parts":[["2024",5,14]]},"author":[{"family":"Botella","given":"Christophe"},{"family":"Dray","given":"Stéphane"},{"family":"Matias","given":"Catherine"},{"family":"Miele","given":"Vincent"},{"family":"Thuiller","given":"Wilfried"}],"citation-key":"botellaAppraisalGraphEmbeddings2022","container-title":"Methods in Ecology and Evolution","DOI":"10.1111/2041-210X.13738","ISSN":"2041-210X","issue":"1","issued":{"date-parts":[["2022"]]},"language":"en","license":"© 2021 British Ecological Society","page":"203–216","source":"Wiley Online Library","title":"An appraisal of graph embeddings for comparing trophic network architectures","type":"article-journal","URL":"https://onlinelibrary.wiley.com/doi/abs/10.1111/2041-210X.13738","volume":"13"},{"id":"braultCoclusteringLatentBloc2015","accessed":{"date-parts":[["2024",5,15]]},"author":[{"family":"Brault","given":"Vincent"},{"family":"Mariadassou","given":"Mahendra"}],"citation-key":"braultCoclusteringLatentBloc2015","container-title":"Journal de la société française de statistique","ISSN":"2102-6238","issue":"3","issued":{"date-parts":[["2015"]]},"language":"fr","page":"120–139","source":"www.numdam.org","title":"Co-clustering through Latent Bloc Model: a Review","title-short":"Co-clustering through Latent Bloc Model","type":"article-journal","URL":"http://www.numdam.org/item/JSFS_2015__156_3_120_0/","volume":"156"},{"id":"braultFastConsistentAlgorithm2023","abstract":"The latent block model is used to simultaneously rank the rows and columns of a matrix to reveal a block structure. The algorithms used for estimation are often time consuming. However, recent work shows that the log-likelihood ratios are equivalent under the complete and observed (with unknown labels) models and the groups posterior distribution to converge as the size of the data increases to a Dirac mass located at the actual groups configuration. Based on these observations, the algorithm Largest Gaps is proposed in this paper to perform clustering using only the marginals of the matrix, when the number of blocks is very small with respect to the size of the whole matrix in the case of binary data. In addition, a model selection method is incorporated with a proof of its consistency. Thus, this paper shows that studying simplistic configurations (few blocks compared to the size of the matrix or very contrasting blocks) with complex algorithms is useless since the marginals already give very good parameter and classification estimates.","accessed":{"date-parts":[["2025",7,9]]},"author":[{"family":"Brault","given":"Vincent"},{"family":"Channarond","given":"Antoine"}],"citation-key":"braultFastConsistentAlgorithm2023","DOI":"10.48550/arXiv.1610.09005","issued":{"date-parts":[["2023",3,9]]},"language":"en","note":"Read_Status: New\nRead_Status_Date: 2025-07-09T13:58:53.533Z","number":"arXiv:1610.09005","publisher":"arXiv","source":"arXiv.org","title":"Fast and Consistent Algorithm for the Latent Block Model","type":"article","URL":"http://arxiv.org/abs/1610.09005"},{"id":"braultGeneralisationLalgorithmeLargest","abstract":"The latent block model assumes there exists a distribution for each crossing between an object cluster and a variable cluster of a data table ; the cells are supposed to be independent conditionally to the choice of these clusters. To estimate the model parameters, most of algorithms are time consuming. Brault and Channarond (2016) proposed to adapt the Largest Gaps algorithm which consists in using the margins. They thus obtained a procedure which estimates all the model parameters consistently but requires a large number of observations. In this talk, we will extend the procedure to the case of any distribution having a second order moment by using an EM algorithm estimation.","author":[{"family":"Brault","given":"Vincent"},{"family":"Channarond","given":"Antoine"},{"family":"Robert","given":"Valérie"}],"citation-key":"braultGeneralisationLalgorithmeLargest","language":"fr","note":"Read_Status: New\nRead_Status_Date: 2025-07-09T12:29:43.098Z","source":"Zotero","title":"Généralisation de l'algorithme Largest Gaps pour le modèle des blocs latents non-paramétrique","type":"article-journal"},{"id":"brehenyPenalizedLikelihood","author":[{"family":"Breheny","given":"Patrick"}],"citation-key":"brehenyPenalizedLikelihood","language":"en","source":"Zotero","title":"Penalized likelihood","type":"article-journal"},{"id":"brosiusMouseModelsDiabetic2009","abstract":"Diabetic nephropathy is a major cause of ESRD worldwide. Despite its prevalence, a lack of reliable animal models that mimic human disease has delayed the identification of specific factors that cause or predict diabetic nephropathy. The Animal Models of Diabetic Complications Consortium (AMDCC) was created in 2001 by the National Institutes of Health to develop and characterize models of diabetic nephropathy and other complications. This interim report and our online supplement detail the progress made toward that goal, specifically in the development and testing of murine models. Updates are provided on validation criteria for early and advanced diabetic nephropathy, phenotyping methods, the effect of background strain on nephropathy, current best models of diabetic nephropathy, negative models, and views of future directions. AMDCC investigators and other investigators in the field have yet to validate a complete murine model of human diabetic kidney disease. Nonetheless, the critical analysis of existing murine models substantially enhances our understanding of this disease process.","accessed":{"date-parts":[["2024",2,6]]},"author":[{"family":"Brosius","given":"Frank C. III"},{"family":"Alpers","given":"Charles E."},{"family":"Bottinger","given":"Erwin P."},{"family":"Breyer","given":"Matthew D."},{"family":"Coffman","given":"Thomas M."},{"family":"Gurley","given":"Susan B."},{"family":"Harris","given":"Raymond C."},{"family":"Kakoki","given":"Masao"},{"family":"Kretzler","given":"Matthias"},{"family":"Leiter","given":"Edward H."},{"family":"Levi","given":"Moshe"},{"family":"McIndoe","given":"Richard A."},{"family":"Sharma","given":"Kumar"},{"family":"Smithies","given":"Oliver"},{"family":"Susztak","given":"Katalin"},{"family":"Takahashi","given":"Nobuyuki"},{"family":"Takahashi","given":"Takamune"},{"family":"Consortium","given":"for the Animal Models of Diabetic Complications"}],"citation-key":"brosiusMouseModelsDiabetic2009","container-title":"Journal of the American Society of Nephrology","DOI":"10.1681/ASN.2009070721","ISSN":"1046-6673","issue":"12","issued":{"date-parts":[["2009",12]]},"language":"en-US","page":"2503","source":"journals.lww.com","title":"Mouse Models of Diabetic Nephropathy","type":"article-journal","URL":"https://journals.lww.com/JASN/fulltext/2009/12000/Mouse_Models_of_Diabetic_Nephropathy.8.aspx","volume":"20"},{"id":"bushmanMolecularGlueUnexpectedly2025","abstract":"Protein structures could guide the design of molecular glue degraders.","accessed":{"date-parts":[["2025",3,10]]},"author":[{"family":"Bushman","given":"Jonathan W."},{"family":"Potts","given":"Patrick Ryan"}],"citation-key":"bushmanMolecularGlueUnexpectedly2025","container-title":"Nature","DOI":"10.1038/d41586-025-00090-7","ISSN":"1476-4687","issue":"8053","issued":{"date-parts":[["2025",3]]},"language":"en","license":"2025 Springer Nature Limited","note":"Bandiera_abtest: a\nCg_type: News And Views\nSubject_term: Drug discovery, Molecular biology, Structural biology, Cancer","page":"42–43","publisher":"Nature Publishing Group","source":"www.nature.com","title":"Molecular glue unexpectedly mimics the effect of cancer mutations","type":"article-journal","URL":"https://www.nature.com/articles/d41586-025-00090-7","volume":"639"},{"id":"bystrovaCausalDiscovery","author":[{"family":"Bystrova","given":"Daria"}],"citation-key":"bystrovaCausalDiscovery","language":"en","note":"Read_Status: New\nRead_Status_Date: 2025-07-02T09:34:39.476Z","title":"Causal discovery","type":"speech"},{"id":"carrascoUncoveringChallengesSolving2025","abstract":"Recently, the Gromov-Wasserstein Optimal Transport (GWOT) problem has attracted the special attention of the ML community. In this problem, given two distributions supported on two (possibly different) spaces, one has to find the most isometric map between them. In the discrete variant of GWOT, the task is to learn an assignment between given discrete sets of points. In the more advanced continuous formulation, one aims at recovering a parametric mapping between unknown continuous distributions based on i.i.d. samples derived from them. The clear geometrical intuition behind the GWOT makes it a natural choice for several practical use cases, giving rise to a number of proposed solvers. Some of them claim to solve the continuous version of the problem. At the same time, GWOT is notoriously hard, both theoretically and numerically. Moreover, all existing continuous GWOT solvers still heavily rely on discrete techniques. Natural questions arise: to what extent do existing methods unravel the GWOT problem, what difficulties do they encounter, and under which conditions they are successful? Our benchmark paper is an attempt to answer these questions. We specifically focus on the continuous GWOT as the most interesting and debatable setup. We crash-test existing continuous GWOT approaches on different scenarios, carefully record and analyze the obtained results, and identify issues. Our findings experimentally testify that the scientific community is still missing a reliable continuous GWOT solver, which necessitates further research efforts. As the first step in this direction, we propose a new continuous GWOT method which does not rely on discrete techniques and partially solves some of the problems of the competitors.","accessed":{"date-parts":[["2025",6,11]]},"author":[{"family":"Carrasco","given":"Xavier Aramayo"},{"family":"Nekrashevich","given":"Maksim"},{"family":"Mokrov","given":"Petr"},{"family":"Burnaev","given":"Evgeny"},{"family":"Korotin","given":"Alexander"}],"citation-key":"carrascoUncoveringChallengesSolving2025","DOI":"10.48550/arXiv.2303.05978","issued":{"date-parts":[["2025",6,4]]},"note":"Read_Status: New\nRead_Status_Date: 2025-06-11T15:49:10.770Z","number":"arXiv:2303.05978","publisher":"arXiv","source":"arXiv.org","title":"Uncovering Challenges of Solving the Continuous Gromov-Wasserstein Problem","type":"article","URL":"http://arxiv.org/abs/2303.05978"},{"id":"cassolKeyFeaturesGuidelines2025","abstract":"Studies of microbial communities vary widely in terms of analysis methods. In this growing field, the wide variety of diversity measures and lack of consistency make it harder to compare different studies. Most existing alpha diversity metrics are inherited from other disciplines and their assumptions are not always directly meaningful or true for microbiome data. Many existing microbiome studies apply one or some alpha diversity metrics with no fundamentals but also an unclear results interpretation. This work focuses on a theoretical, empirical, and comparative analysis of 19 frequently and less-frequently used microbial alpha diversity metrics grouped into 4 proposed categories, including key features of every analyzed metric with their mathematical assumptions, to provide a deeper understanding of the existing metrics and a practical implementation guide for future studies. Key metrics that should be required in microbiome analysis include richness, phylogenetic diversity, entropy, dominance of a few microbes over others, and an estimate of unobserved microbes. Collectively, these metrics contribute to a comprehensive set of analyses characterizing samples, allowing the determination of key aspects that might be otherwise obscured by partial or biased information. These guidelines enable further detailed analysis by each author according to their specific interests and clinical trials. Several practical examples are provided to illustrate how these recommendations improve the quality and depth of information obtained, facilitating better interpretation when working with microbiome data. These guidelines can be applied to both existing and future research studies, enhancing the standardization, consistency, and robustness of the analyses conducted. This approach aims to improve the capture of biological diversity, leading to better interpretations and insights.","accessed":{"date-parts":[["2025",8,18]]},"author":[{"family":"Cassol","given":"Ignacio"},{"family":"Ibañez","given":"Mauro"},{"family":"Bustamante","given":"Juan Pablo"}],"citation-key":"cassolKeyFeaturesGuidelines2025","container-title":"Scientific Reports","container-title-short":"Sci Rep","DOI":"10.1038/s41598-024-77864-y","ISSN":"2045-2322","issue":"1","issued":{"date-parts":[["2025",1,3]]},"language":"en","license":"2025 The Author(s)","note":"Read_Status: New\nRead_Status_Date: 2025-08-18T15:11:42.130Z","page":"622","publisher":"Nature Publishing Group","source":"www.nature.com","title":"Key features and guidelines for the application of microbial alpha diversity metrics","type":"article-journal","URL":"https://www.nature.com/articles/s41598-024-77864-y","volume":"15"},{"id":"celisseConsistencyMaximumlikelihoodVariational2012","abstract":"The stochastic block model (SBM) is a probabilistic model designed to describe heterogeneous directed and undirected graphs. In this paper, we address the asymptotic inference in SBM by use of maximum-likelihood and variational approaches. The identifiability of SBM is proved while asymptotic properties of maximum-likelihood and variational estimators are derived. In particular, the consistency of these estimators is settled for the probability of an edge between two vertices (and for the group proportions at the price of an additional assumption), which is to the best of our knowledge the first result of this type for variational estimators in random graphs.","accessed":{"date-parts":[["2023",6,6]]},"author":[{"family":"Celisse","given":"Alain"},{"family":"Daudin","given":"Jean-Jacques"},{"family":"Pierre","given":"Laurent"}],"citation-key":"celisseConsistencyMaximumlikelihoodVariational2012","container-title":"Electronic Journal of Statistics","DOI":"10.1214/12-EJS729","ISSN":"1935-7524, 1935-7524","issue":"none","issued":{"date-parts":[["2012",1]]},"page":"1847–1899","publisher":"Institute of Mathematical Statistics and Bernoulli Society","title":"Consistency of maximum-likelihood and variational estimators in the stochastic block model","type":"article-journal","URL":"https://projecteuclid.org/journals/electronic-journal-of-statistics/volume-6/issue-none/Consistency-of-maximum-likelihood-and-variational-estimators-in-the-stochastic/10.1214/12-EJS729.full","volume":"6"},{"id":"celisseConsistencyMaximumlikelihoodVariational2012a","abstract":"The stochastic block model (SBM) is a probabilistic model designed to describe heterogeneous directed and undirected graphs. In this paper, we address the asymptotic inference in SBM by use of maximum-likelihood and variational approaches. The identifiability of SBM is proved while asymptotic properties of maximum-likelihood and variational estimators are derived. In particular, the consistency of these estimators is settled for the probability of an edge between two vertices (and for the group proportions at the price of an additional assumption), which is to the best of our knowledge the first result of this type for variational estimators in random graphs.","accessed":{"date-parts":[["2023",6,6]]},"author":[{"family":"Celisse","given":"Alain"},{"family":"Daudin","given":"Jean-Jacques"},{"family":"Pierre","given":"Laurent"}],"citation-key":"celisseConsistencyMaximumlikelihoodVariational2012a","container-title":"Electronic Journal of Statistics","DOI":"10.1214/12-EJS729","ISSN":"1935-7524, 1935-7524","issue":"none","issued":{"date-parts":[["2012",1]]},"page":"1847–1899","publisher":"Institute of Mathematical Statistics and Bernoulli Society","source":"Project Euclid","title":"Consistency of maximum-likelihood and variational estimators in the stochastic block model","type":"article-journal","URL":"https://projecteuclid.org/journals/electronic-journal-of-statistics/volume-6/issue-none/Consistency-of-maximum-likelihood-and-variational-estimators-in-the-stochastic/10.1214/12-EJS729.full","volume":"6"},{"id":"chabert-liddellLearningCommonStructures2023","abstract":"Let a collection of networks represent interactions within several (social or ecological) systems. We pursue two objectives: identifying similarities in the topological structures that are held in common between the networks and clustering the collection into sub-collections of structurally homogeneous networks. We tackle these two questions with a probabilistic model based approach. We propose an extension of the Stochastic Block Model (SBM) adapted to the joint modeling of a collection of networks. The networks in the collection are assumed to be independent realizations of SBMs. The common connectivity structure is imposed through the equality of some parameters. The model parameters are estimated with a variational Expectation-Maximization (EM) algorithm. We derive an ad-hoc penalized likelihood criterion to select the number of blocks and to assess the adequacy of the consensus found between the structures of the different networks. This same criterion can also be used to cluster networks on the basis of their connectivity structure. It thus provides a partition of the collection into subsets of structurally homogeneous networks. The relevance of our proposition is assessed on two collections of ecological networks. First, an application to three stream food webs reveals the homogeneity of their structures and the correspondence between groups of species in different ecosystems playing equivalent ecological roles. Moreover, the joint analysis allows a finer analysis of the structure of smaller networks. Second, we cluster 67 food webs according to their connectivity structures and demonstrate that five mesoscale structures are sufficient to describe this collection.","accessed":{"date-parts":[["2023",5,22]]},"author":[{"family":"Chabert-Liddell","given":"Saint-Clair"},{"family":"Barbillon","given":"Pierre"},{"family":"Donnet","given":"Sophie"}],"citation-key":"chabert-liddellLearningCommonStructures2023","DOI":"10.48550/arXiv.2206.00560","genre":"article","issued":{"date-parts":[["2023",3,27]]},"number":"arXiv:2206.00560","publisher":"arXiv","source":"arXiv.org","title":"Learning common structures in a collection of networks. An application to food webs","type":"article","URL":"http://arxiv.org/abs/2206.00560"},{"id":"chabert-liddellLearningCommonStructures2024","abstract":"Let a collection of networks represent interactions within several (social or ecological) systems. We pursue two objectives: identifying similarities in the topological structures that are held in common between the networks and clustering the collection into subcollections of structurally homogeneous networks. We tackle these two questions with a probabilistic model-based approach. We propose an extension of the stochastic block model (SBM) adapted to the joint modeling of a collection of networks. The networks in the collection are assumed to be independent realizations of SBMs. The common connectivity structure is imposed through the equality of some parameters. The model parameters are estimated with a variational expectation-maximization (EM) algorithm. We derive an ad hoc penalized likelihood criterion to select the number of blocks and to assess the adequacy of the consensus found between the structures of the different networks. This same criterion can also be used to cluster networks on the basis of their connectivity structure. It thus provides a partition of the collection into subsets of structurally homogeneous networks. The relevance of our proposition is assessed on two collections of ecological networks. First, an application to three stream food webs reveals the homogeneity of their structures and the correspondence between groups of species in different ecosystems playing equivalent ecological roles. Moreover, the joint analysis allows a finer analysis of the structure of smaller networks. Second, we cluster 67 food webs according to their connectivity structures and demonstrate that five mesoscale structures are sufficient to describe this collection.","accessed":{"date-parts":[["2024",7,1]]},"author":[{"family":"Chabert-Liddell","given":"Saint-Clair"},{"family":"Barbillon","given":"Pierre"},{"family":"Donnet","given":"Sophie"}],"citation-key":"chabert-liddellLearningCommonStructures2024","container-title":"The Annals of Applied Statistics","DOI":"10.1214/23-AOAS1831","ISSN":"1932-6157, 1941-7330","issue":"2","issued":{"date-parts":[["2024",6]]},"page":"1213–1235","publisher":"Institute of Mathematical Statistics","title":"Learning common structures in a collection of networks. An application to food webs","type":"article-journal","URL":"https://projecteuclid.org/journals/annals-of-applied-statistics/volume-18/issue-2/Learning-common-structures-in-a-collection-of-networks-An-application/10.1214/23-AOAS1831.full","volume":"18"},{"id":"chabert-liddellLearningCommonStructures2024a","abstract":"Let a collection of networks represent interactions within several (social or ecological) systems. We pursue two objectives: identifying similarities in the topological structures that are held in common between the networks and clustering the collection into subcollections of structurally homogeneous networks. We tackle these two questions with a probabilistic model-based approach. We propose an extension of the stochastic block model (SBM) adapted to the joint modeling of a collection of networks. The networks in the collection are assumed to be independent realizations of SBMs. The common connectivity structure is imposed through the equality of some parameters. The model parameters are estimated with a variational expectation-maximization (EM) algorithm. We derive an ad hoc penalized likelihood criterion to select the number of blocks and to assess the adequacy of the consensus found between the structures of the different networks. This same criterion can also be used to cluster networks on the basis of their connectivity structure. It thus provides a partition of the collection into subsets of structurally homogeneous networks. The relevance of our proposition is assessed on two collections of ecological networks. First, an application to three stream food webs reveals the homogeneity of their structures and the correspondence between groups of species in different ecosystems playing equivalent ecological roles. Moreover, the joint analysis allows a finer analysis of the structure of smaller networks. Second, we cluster 67 food webs according to their connectivity structures and demonstrate that five mesoscale structures are sufficient to describe this collection.","accessed":{"date-parts":[["2024",5,16]]},"author":[{"family":"Chabert-Liddell","given":"Saint-Clair"},{"family":"Barbillon","given":"Pierre"},{"family":"Donnet","given":"Sophie"}],"citation-key":"chabert-liddellLearningCommonStructures2024a","container-title":"The Annals of Applied Statistics","DOI":"10.1214/23-AOAS1831","ISSN":"1932-6157, 1941-7330","issue":"2","issued":{"date-parts":[["2024",6]]},"page":"1213–1235","publisher":"Institute of Mathematical Statistics","source":"Project Euclid","title":"Learning common structures in a collection of networks. An application to food webs","type":"article-journal","URL":"https://projecteuclid.org/journals/annals-of-applied-statistics/volume-18/issue-2/Learning-common-structures-in-a-collection-of-networks-An-application/10.1214/23-AOAS1831.full","volume":"18"},{"id":"chabert-liddellLearningCommonStructures2024b","abstract":"Let a collection of networks represent interactions within several (social or ecological) systems. We pursue two objectives: identifying similarities in the topological structures that are held in common between the networks and clustering the collection into subcollections of structurally homogeneous networks. We tackle these two questions with a probabilistic model-based approach. We propose an extension of the stochastic block model (SBM) adapted to the joint modeling of a collection of networks. The networks in the collection are assumed to be independent realizations of SBMs. The common connectivity structure is imposed through the equality of some parameters. The model parameters are estimated with a variational expectation-maximization (EM) algorithm. We derive an ad hoc penalized likelihood criterion to select the number of blocks and to assess the adequacy of the consensus found between the structures of the different networks. This same criterion can also be used to cluster networks on the basis of their connectivity structure. It thus provides a partition of the collection into subsets of structurally homogeneous networks. The relevance of our proposition is assessed on two collections of ecological networks. First, an application to three stream food webs reveals the homogeneity of their structures and the correspondence between groups of species in different ecosystems playing equivalent ecological roles. Moreover, the joint analysis allows a finer analysis of the structure of smaller networks. Second, we cluster 67 food webs according to their connectivity structures and demonstrate that five mesoscale structures are sufficient to describe this collection.","accessed":{"date-parts":[["2024",7,1]]},"author":[{"family":"Chabert-Liddell","given":"Saint-Clair"},{"family":"Barbillon","given":"Pierre"},{"family":"Donnet","given":"Sophie"}],"citation-key":"chabert-liddellLearningCommonStructures2024b","container-title":"The Annals of Applied Statistics","DOI":"10.1214/23-AOAS1831","ISSN":"1932-6157, 1941-7330","issue":"2","issued":{"date-parts":[["2024",6]]},"page":"1213–1235","publisher":"Institute of Mathematical Statistics","source":"Project Euclid","title":"Learning common structures in a collection of networks. An application to food webs","type":"article-journal","URL":"https://projecteuclid.org/journals/annals-of-applied-statistics/volume-18/issue-2/Learning-common-structures-in-a-collection-of-networks-An-application/10.1214/23-AOAS1831.full","volume":"18"},{"id":"chabert-liddellStatisticalLearningCollections","author":[{"family":"Chabert-Liddell","given":"Saint-Clair"}],"citation-key":"chabert-liddellStatisticalLearningCollections","language":"fr","note":"Read_Status: New\nRead_Status_Date: 2025-05-20T12:28:31.466Z","source":"Zotero","title":"Statistical learning of collections of networks with applications in ecology and sociology","type":"article-journal"},{"id":"chabert-liddellStochasticBlockModel2021","abstract":"A multilevel network is defined as the junction of two interaction networks, one level representing the interactions between individuals and the other the interactions between organizations. The levels are linked by an affiliation relationship, each individual belonging to a unique organization. A new Stochastic Block Model is proposed as a unified probalistic framework tailored for multilevel networks. This model contains latent blocks accounting for heterogeneity in the patterns of connection within each level and introducing dependencies between the levels. The sought connection patterns are not specified a priori which makes this approach flexible. Variational methods are used for the model inference and an Integrated Classified Likelihood criterion is developed for choosing the number of blocks and also for deciding whether the two levels are dependent or not. A comprehensive simulation study exhibits the benefit of considering this approach, illustrates the robustness of the clustering and highlights the reliability of the criterion used for model selection. This approach is applied on a sociological dataset collected during a television program trade fair, the inter-organizational level being the economic network between companies and the inter-individual level being the informal network between their representatives. It brings a synthetic representation of the two networks unraveling their intertwined structure and confirms the coopetition at stake.","accessed":{"date-parts":[["2025",9,26]]},"author":[{"family":"Chabert-Liddell","given":"Saint-Clair"},{"family":"Barbillon","given":"Pierre"},{"family":"Donnet","given":"Sophie"},{"family":"Lazega","given":"Emmanuel"}],"citation-key":"chabert-liddellStochasticBlockModel2021","container-title":"Computational Statistics & Data Analysis","container-title-short":"Computational Statistics & Data Analysis","DOI":"10.1016/j.csda.2021.107179","ISSN":"01679473","issued":{"date-parts":[["2021",6]]},"note":"Read_Status: New\nRead_Status_Date: 2025-09-26T08:52:07.522Z","page":"107179","source":"arXiv.org","title":"A Stochastic Block Model Approach for the Analysis of Multilevel Networks: an Application to the Sociology of Organizations","title-short":"A Stochastic Block Model Approach for the Analysis of Multilevel Networks","type":"article-journal","URL":"http://arxiv.org/abs/1910.10512","volume":"158"},{"id":"chaffronCommunityNetworkModels","author":[{"family":"Chaffron","given":"Samuel"}],"citation-key":"chaffronCommunityNetworkModels","language":"en","note":"Read_Status: New\nRead_Status_Date: 2025-11-28T12:53:30.140Z","source":"Zotero","title":"Community network models to reveal marine plankton systems ecology and evolution","type":"article-journal"},{"id":"channarondClassificationEstimationStochastic2012","abstract":"The Stochastic Blockmodel [16] is a mixture model for heterogeneous network data. Unlike the usual statistical framework, new nodes give additional information about the previous ones in this model. Thereby the distribution of the degrees concentrates in points conditionally on the node class. We show under a mild assumption that classification, estimation and model selection can actually be achieved with no more than the empirical degree data. We provide an algorithm able to process very large networks and consistent estimators based on it. In particular, we prove a bound of the probability of misclassification of at least one node, including when the number of classes grows.","accessed":{"date-parts":[["2025",7,9]]},"author":[{"family":"Channarond","given":"Antoine"},{"family":"Daudin","given":"Jean-Jacques"},{"family":"Robin","given":"Stéphane"}],"citation-key":"channarondClassificationEstimationStochastic2012","container-title":"Electronic Journal of Statistics","container-title-short":"Electron. J. Statist.","DOI":"10.1214/12-ejs753","ISSN":"1935-7524","issue":"none","issued":{"date-parts":[["2012",1,1]]},"language":"en","note":"Read_Status: New\nRead_Status_Date: 2025-07-09T13:59:33.921Z","publisher":"Institute of Mathematical Statistics","source":"Crossref","title":"Classification and estimation in the Stochastic Blockmodel based on the empirical degrees","type":"article-journal","URL":"https://projecteuclid.org/journals/electronic-journal-of-statistics/volume-6/issue-none/Classification-and-estimation-in-the-Stochastic-Blockmodel-based-on-the/10.1214/12-EJS753.full","volume":"6"},{"id":"ChatGPT","abstract":"Un système d’IA conversationnel qui écoute, apprend et vous pousse à réfléchir","accessed":{"date-parts":[["2025",10,22]]},"citation-key":"ChatGPT","container-title":"ChatGPT","language":"fr-FR","note":"Read_Status: New\nRead_Status_Date: 2025-10-22T08:06:41.132Z","title":"ChatGPT","type":"webpage","URL":"https://chatgpt.com/fr-FR/"},{"id":"chaussardTreebasedVariationalInference2025","abstract":"When studying ecosystems, hierarchical trees are often used to organize entities based on proximity criteria, such as the taxonomy in microbiology, social classes in geography, or product types in retail businesses, offering valuable insights into entity relationships. Despite their significance, current count-data models do not leverage this structured information. In particular, the widely used Poisson log-normal (PLN) model, known for its ability to model interactions between entities from count data, lacks the possibility to incorporate such hierarchical tree structures, limiting its applicability in domains characterized by such complexities. To address this matter, we introduce the PLN-Tree model as an extension of the PLN model, specifically designed for modeling hierarchical count data. By integrating structured variational inference techniques, we propose an adapted training procedure and establish identifiability results, enhancing both theoretical foundations and practical interpretability. Experiments on synthetic datasets and human gut microbiome data highlight generative improvements when using PLN-Tree, demonstrating the practical interest of knowledge graphs like the taxonomy in microbiome modeling. Additionally, we present a proof-of-concept implication of the identifiability results by illustrating the practical benefits of using identifiable features for classification tasks, showcasing the versatility of the framework.","accessed":{"date-parts":[["2025",10,22]]},"author":[{"family":"Chaussard","given":"Alexandre"},{"family":"Bonnet","given":"Anna"},{"family":"Gassiat","given":"Elisabeth"},{"family":"Corff","given":"Sylvain Le"}],"citation-key":"chaussardTreebasedVariationalInference2025","DOI":"10.48550/arXiv.2406.17361","issued":{"date-parts":[["2025",6,26]]},"note":"Read_Status: New\nRead_Status_Date: 2025-10-22T15:07:42.419Z","number":"arXiv:2406.17361","source":"arXiv.org","title":"Tree-based variational inference for Poisson log-normal models","type":"article","URL":"http://arxiv.org/abs/2406.17361"},{"id":"chenAssociatingMicrobiomeComposition2012","abstract":"Motivation: The human microbiome plays an important role in human disease and health. Identification of factors that affect the microbiome composition can provide insights into disease mechanism as well as suggest ways to modulate the microbiome composition for therapeutical purposes. Distance-based statistical tests have been applied to test the association of microbiome composition with environmental or biological covariates. The unweighted and weighted UniFrac distances are the most widely used distance measures. However, these two measures assign too much weight either to rare lineages or to most abundant lineages, which can lead to loss of power when the important composition change occurs in moderately abundant lineages.","accessed":{"date-parts":[["2025",11,7]]},"author":[{"family":"Chen","given":"Jun"},{"family":"Bittinger","given":"Kyle"},{"family":"Charlson","given":"Emily S."},{"family":"Hoffmann","given":"Christian"},{"family":"Lewis","given":"James"},{"family":"Wu","given":"Gary D."},{"family":"Collman","given":"Ronald G."},{"family":"Bushman","given":"Frederic D."},{"family":"Li","given":"Hongzhe"}],"citation-key":"chenAssociatingMicrobiomeComposition2012","container-title":"Bioinformatics","DOI":"10.1093/bioinformatics/bts342","ISSN":"1367-4811, 1367-4803","issue":"16","issued":{"date-parts":[["2012",8,15]]},"language":"en","license":"http://creativecommons.org/licenses/by-nc/3.0","note":"Read_Status: New\nRead_Status_Date: 2025-11-07T14:53:58.882Z","page":"2106–2113","source":"DOI.org (Crossref)","title":"Associating microbiome composition with environmental covariates using generalized UniFrac distances","type":"article-journal","URL":"https://academic.oup.com/bioinformatics/article/28/16/2106/324465","volume":"28"},{"id":"chenQuantitativeFrameworkCharacterizing2019","abstract":"The evolutionary history of a gene helps predict its function and relationship to phenotypic traits. Although sequence conservation is commonly used to decipher gene function and assess medical relevance, methods for functional inference from comparative expression data are lacking. Here, we use RNA-seq across seven tissues from 17 mammalian species to show that expression evolution across mammals is accurately modeled by the Ornstein-Uhlenbeck process, a commonly proposed model of continuous trait evolution. We apply this model to identify expression pathways under neutral, stabilizing, and directional selection. We further demonstrate novel applications of this model to quantify the extent of stabilizing selection on a gene's expression, parameterize the distribution of each gene's optimal expression level, and detect deleterious expression levels in expression data from individual patients. Our work provides a statistical framework for interpreting expression data across species and in disease.","author":[{"family":"Chen","given":"Jenny"},{"family":"Swofford","given":"Ross"},{"family":"Johnson","given":"Jeremy"},{"family":"Cummings","given":"Beryl B."},{"family":"Rogel","given":"Noga"},{"family":"Lindblad-Toh","given":"Kerstin"},{"family":"Haerty","given":"Wilfried"},{"family":"Palma","given":"Federica","dropping-particle":"di"},{"family":"Regev","given":"Aviv"}],"citation-key":"chenQuantitativeFrameworkCharacterizing2019","container-title":"Genome Research","container-title-short":"Genome Res","DOI":"10.1101/gr.237636.118","ISSN":"1549-5469","issue":"1","issued":{"date-parts":[["2019",1]]},"language":"eng","page":"53–63","PMCID":"PMC6314168","PMID":"30552105","source":"PubMed","title":"A quantitative framework for characterizing the evolutionary history of mammalian gene expression","type":"article-journal","volume":"29"},{"id":"chiquetFastTreeInference2015","abstract":"Given a data set with many features observed in a large number of conditions, it is desirable to fuse and aggregate conditions which are similar to ease the interpretation and extract the main characteristics of the data. This paper presents a multidimensional fusion penalty framework to address this question when the number of conditions is large. If the fusion penalty is encoded by an `q-norm, we prove for uniform weights that the path of solutions is a tree which is suitable for interpretability. For the `1 and `∞-norms, the path is piecewise linear and we derive a homotopy algorithm to recover exactly the whole tree structure. For weighted `1-fusion penalties, we demonstrate that distance-decreasing weights lead to balanced tree structures. For a subclass of these weights that we call “exponentially adaptive”, we derive an O(n log(n)) homotopy algorithm and we prove an asymptotic oracle property. This guarantees that we recover the underlying structure of the data efficiently both from a statistical and a computational point of view. We provide a fast implementation of the homotopy algorithm for the single feature case, as well as an efficient embedded cross-validation procedure that takes advantage of the tree structure of the path of solutions. Our proposal outperforms its competing procedures on simulations both in terms of timings and prediction accuracy. As an example we consider phenotypic data: given one or several traits, we reconstruct a balanced tree structure and assess its agreement with the known taxonomy.","accessed":{"date-parts":[["2026",1,7]]},"author":[{"family":"Chiquet","given":"Julien"},{"family":"Gutierrez","given":"Pierre"},{"family":"Rigaill","given":"Guillem"}],"citation-key":"chiquetFastTreeInference2015","DOI":"10.48550/arXiv.1407.5915","issued":{"date-parts":[["2015",5,27]]},"language":"en","note":"Read_Status: New\nRead_Status_Date: 2026-01-07T14:25:09.306Z","number":"arXiv:1407.5915","publisher":"arXiv","source":"arXiv.org","title":"Fast tree inference with weighted fusion penalties","type":"article","URL":"http://arxiv.org/abs/1407.5915"},{"id":"chiquetSbmStochasticBlockmodels2024","abstract":"A collection of tools and functions to adjust a variety of stochastic blockmodels (SBM). Supports at the moment Simple, Bipartite, 'Multipartite' and Multiplex SBM (undirected or directed with Bernoulli, Poisson or Gaussian emission laws on the edges, and possibly covariate for Simple and Bipartite SBM). See Léger (2016) <doi:10.48550/arXiv.1602.07587>, 'Barbillon et al.' (2020) <doi:10.1111/rssa.12193> and 'Bar-Hen et al.' (2020) <doi:10.48550/arXiv.1807.10138>.","accessed":{"date-parts":[["2024",11,4]]},"author":[{"family":"Chiquet","given":"Julien"},{"family":"Donnet","given":"Sophie"},{"family":"Barbillon","given":"Pierre"}],"citation-key":"chiquetSbmStochasticBlockmodels2024","issued":{"date-parts":[["2024",9,16]]},"license":"GPL (≥ 3)","source":"R-Packages","title":"sbm: Stochastic Blockmodels","title-short":"sbm","type":"software","URL":"https://cran.r-project.org/web/packages/sbm/index.html","version":"0.4.7"},{"id":"chiquetSbmStochasticBlockmodels2024","abstract":"A collection of tools and functions to adjust a variety of stochastic blockmodels (SBM). Supports at the moment Simple, Bipartite, 'Multipartite' and Multiplex SBM (undirected or directed with Bernoulli, Poisson or Gaussian emission laws on the edges, and possibly covariate for Simple and Bipartite SBM). See Léger (2016) <doi:10.48550/arXiv.1602.07587>, 'Barbillon et al.' (2020) <doi:10.1111/rssa.12193> and 'Bar-Hen et al.' (2020) <doi:10.48550/arXiv.1807.10138>.","accessed":{"date-parts":[["2024",11,4]]},"author":[{"family":"Chiquet","given":"Julien"},{"family":"Donnet","given":"Sophie"},{"family":"Barbillon","given":"Pierre"}],"citation-key":"chiquetSbmStochasticBlockmodels2024","issued":{"date-parts":[["2024",9,16]]},"title":"Sbm: Stochastic Blockmodels","title-short":"Sbm","type":"software","URL":"https://cran.r-project.org/web/packages/sbm/index.html","version":"0.4.7"},{"id":"clausetHierarchicalStructurePrediction2008","abstract":"Networks are now a ubiquitous tool for representing the structure of complex systems, including the Internet, social networks, food webs, and protein and genetic networks. Unfortunately, the data describing these networks are in many cases incomplete or biased. A new study provides a general technique to divide network vertices into groups and sub-groups. Revealing such underlying hierarchies makes it possible to predict missing links from partial data with higher accuracy than previous methods.","accessed":{"date-parts":[["2025",9,19]]},"author":[{"family":"Clauset","given":"Aaron"},{"family":"Moore","given":"Cristopher"},{"family":"Newman","given":"M. E. J."}],"citation-key":"clausetHierarchicalStructurePrediction2008","container-title":"Nature","DOI":"10.1038/nature06830","ISSN":"1476-4687","issue":"7191","issued":{"date-parts":[["2008",5]]},"language":"english","page":"98–101","publisher":"Nature Publishing Group","title":"Hierarchical structure and the prediction of missing links in networks","type":"article-journal","URL":"https://www.nature.com/articles/nature06830","volume":"453"},{"id":"clausetHierarchicalStructurePrediction2008a","abstract":"Networks are now a ubiquitous tool for representing the structure of complex systems, including the Internet, social networks, food webs, and protein and genetic networks. Unfortunately, the data describing these networks are in many cases incomplete or biased. A new study provides a general technique to divide network vertices into groups and sub-groups. Revealing such underlying hierarchies makes it possible to predict missing links from partial data with higher accuracy than previous methods.","accessed":{"date-parts":[["2025",9,19]]},"author":[{"family":"Clauset","given":"Aaron"},{"family":"Moore","given":"Cristopher"},{"family":"Newman","given":"M. E. J."}],"citation-key":"clausetHierarchicalStructurePrediction2008a","container-title":"Nature","DOI":"10.1038/nature06830","ISSN":"1476-4687","issue":"7191","issued":{"date-parts":[["2008",5]]},"language":"en","license":"2008 Springer Nature Limited","note":"Read_Status: New\nRead_Status_Date: 2025-09-19T12:33:29.962Z","page":"98–101","publisher":"Nature Publishing Group","source":"www-nature-com.ezproxy.universite-paris-saclay.fr","title":"Hierarchical structure and the prediction of missing links in networks","type":"article-journal","URL":"https://www.nature.com/articles/nature06830","volume":"453"},{"id":"ClusteringLargeApplications1990","abstract":"The prelims comprise: Short Description of the Method How to Use the Program CLARA An Example More on the Algorithm and the Program Related Methods and References","accessed":{"date-parts":[["2024",9,13]]},"citation-key":"ClusteringLargeApplications1990","container-title":"Finding Groups in Data","DOI":"10.1002/9780470316801.ch3","ISBN":"978-0-470-31680-1","issued":{"date-parts":[["1990"]]},"language":"en","page":"126–163","publisher":"John Wiley & Sons, Ltd","section":"3","source":"Wiley Online Library","title":"Clustering Large Applications (Program CLARA)","type":"chapter","URL":"https://onlinelibrary.wiley.com/doi/abs/10.1002/9780470316801.ch3"},{"id":"colettaALMAGALIIICompact2025","abstract":"The mechanisms behind the fragmentation of high-mass dense clumps into compact star-forming cores are fundamental topics in current astrophysical research. The ALMAGAL survey provides the opportunity to study this process at an unprecedented level of detail and statistical significance, featuring high-angular resolution $1.38$ mm ALMA observations of $1013$ massive dense clumps at various Galactic locations. These clumps cover a wide range of distances, masses, surface densities, and evolutionary stages. Here, we present the catalog of compact sources obtained with the CuTEx algorithm from continuum images of the full ALMAGAL clump sample combining ACA-$7$m and $12$m ALMA arrays, reaching a uniform high median spatial resolution of $\\sim1400$ au. We discuss the fragmentation properties and the estimated physical parameters of the core population. The ALMAGAL compact source catalog includes $6348$ cores detected in $844$ clumps ($83\\%$ of the total), with a number of cores per clump between $1$ and $49$ (median of $5$). The estimated core diameters are mostly within $\\sim800-3000$ au (median of $1700$ au). We obtained core masses from $0.002$ to $345\\,\\mathrm{M_{\\odot}}$. We evaluated the variation in the core mass function (CMF) with evolution as traced by the clump $L/M$, finding a clear, robust shift and change in slope among CMFs within subsamples at different stages. This finding suggests that the CMF shape is not constant throughout the star formation process, but rather it builds (and flattens) with evolution, with higher core masses reached at later stages. We found that all cores within a clump grow in mass on average with evolution, and the number of cores increases with the core masses. Our results favor a clump-fed scenario for high-mass star formation, in which cores form as low-mass seeds, and then gain mass while further fragmentation occurs in the clump.","accessed":{"date-parts":[["2025",3,10]]},"author":[{"family":"Coletta","given":"A."},{"family":"Molinari","given":"S."},{"family":"Schisano","given":"E."},{"family":"Traficante","given":"A."},{"family":"Elia","given":"D."},{"family":"Benedettini","given":"M."},{"family":"Mininni","given":"C."},{"family":"Soler","given":"J. D."},{"family":"Sánchez-Monge","given":"Á"},{"family":"Schilke","given":"P."},{"family":"Battersby","given":"C."},{"family":"Fuller","given":"G. A."},{"family":"Beuther","given":"H."},{"family":"Zhang","given":"Q."},{"family":"Beltrán","given":"M. T."},{"family":"Jones","given":"B."},{"family":"Klessen","given":"R. S."},{"family":"Walch","given":"S."},{"family":"Fontani","given":"F."},{"family":"Avison","given":"A."},{"family":"Brogan","given":"C. L."},{"family":"Clarke","given":"S. D."},{"family":"Hatchfield","given":"P."},{"family":"Hennebelle","given":"P."},{"family":"Ho","given":"P. T."},{"family":"Hunter","given":"T. R."},{"family":"Johnston","given":"K. G."},{"family":"Klaassen","given":"P. D."},{"family":"Koch","given":"P. M."},{"family":"Kuiper","given":"R."},{"family":"Lis","given":"D. C."},{"family":"Liu","given":"T."},{"family":"Lumsden","given":"S. L."},{"family":"Maruccia","given":"Y."},{"family":"Möller","given":"T."},{"family":"Moscadelli","given":"L."},{"family":"Nucara","given":"A."},{"family":"Rigby","given":"A. J."},{"family":"Rygl","given":"K. L. J."},{"family":"Sanhueza","given":"P."},{"family":"Tak","given":"F.","dropping-particle":"van der"},{"family":"Wells","given":"M. R. A."},{"family":"Wyrowski","given":"F."},{"family":"Angelis","given":"F. De"},{"family":"Liu","given":"S."},{"family":"Ahmadi","given":"A."},{"family":"Bronfman","given":"L."},{"family":"Liu","given":"S.-Y."},{"family":"Su","given":"Y.-N."},{"family":"Tang","given":"Y."},{"family":"Testi","given":"L."},{"family":"Zinnecker","given":"H."}],"citation-key":"colettaALMAGALIIICompact2025","DOI":"10.48550/arXiv.2503.05663","issued":{"date-parts":[["2025",3,7]]},"number":"arXiv:2503.05663","publisher":"arXiv","source":"arXiv.org","title":"ALMAGAL III. Compact source catalog: Fragmentation statistics and physical evolution of the core population","title-short":"ALMAGAL III. Compact source catalog","type":"article","URL":"http://arxiv.org/abs/2503.05663"},{"id":"corsoConnectivityNestednessBipartite2011","abstract":"Bipartite networks and the nestedness concept appear in two different contexts in theoretical ecology: community ecology and islands biogeography. From a mathematical perspective nestedness is a pattern in a bipartite network. There are several nestedness indices in the market, we used the index ν. The index ν is found using the relation ν = 1 − τ where τ is the temperature of the adjacency matrix of the bipartite network. By its turn τ is defined with help of the Manhattan distance of the occupied elements of the adjacency matrix of the bipartite network. We prove that the nestedness index ν is a function of the connectivities of the bipartite network. In addition we find a concise way to find ν which avoid cumbersome algorithm manupulation of the adjacency matrix.","accessed":{"date-parts":[["2024",11,5]]},"author":[{"family":"Corso","given":"Gilberto"},{"family":"De Araujo","given":"A I Levartoski"},{"family":"De Almeida","given":"Adriana M"}],"citation-key":"corsoConnectivityNestednessBipartite2011","container-title":"Journal of Physics: Conference Series","container-title-short":"J. Phys.: Conf. Ser.","DOI":"10.1088/1742-6596/285/1/012009","ISSN":"1742-6596","issued":{"date-parts":[["2011",3,1]]},"language":"en","page":"012009","source":"DOI.org (Crossref)","title":"Connectivity and Nestedness in Bipartite Networks from Community Ecology","type":"article-journal","URL":"https://iopscience.iop.org/article/10.1088/1742-6596/285/1/012009","volume":"285"},{"id":"corsoConnectivityNestednessBipartite2011","abstract":"Bipartite networks and the nestedness concept appear in two different contexts in theoretical ecology: community ecology and islands biogeography. From a mathematical perspective nestedness is a pattern in a bipartite network. There are several nestedness indices in the market, we used the index ν. The index ν is found using the relation ν = 1 − τ where τ is the temperature of the adjacency matrix of the bipartite network. By its turn τ is defined with help of the Manhattan distance of the occupied elements of the adjacency matrix of the bipartite network. We prove that the nestedness index ν is a function of the connectivities of the bipartite network. In addition we find a concise way to find ν which avoid cumbersome algorithm manupulation of the adjacency matrix.","accessed":{"date-parts":[["2024",11,5]]},"author":[{"family":"Corso","given":"Gilberto"},{"family":"De Araujo","given":"A I Levartoski"},{"family":"De Almeida","given":"Adriana M"}],"citation-key":"corsoConnectivityNestednessBipartite2011","container-title":"Journal of Physics: Conference Series","container-title-short":"J. Phys.: Conf. Ser.","DOI":"10.1088/1742-6596/285/1/012009","ISSN":"1742-6596","issued":{"date-parts":[["2011",3,1]]},"language":"english","page":"012009","title":"Connectivity and Nestedness in Bipartite Networks from Community Ecology","type":"article-journal","URL":"https://iopscience.iop.org/article/10.1088/1742-6596/285/1/012009","volume":"285"},{"id":"corsoConnectivityNestednessBipartite2011a","abstract":"Bipartite networks and the nestedness concept appear in two different contexts in theoretical ecology: community ecology and islands biogeography. From a mathematical perspective nestedness is a pattern in a bipartite network. There are several nestedness indices in the market, we used the index ν. The index ν is found using the relation ν = 1 − τ where τ is the temperature of the adjacency matrix of the bipartite network. By its turn τ is defined with help of the Manhattan distance of the occupied elements of the adjacency matrix of the bipartite network. We prove that the nestedness index ν is a function of the connectivities of the bipartite network. In addition we find a concise way to find ν which avoid cumbersome algorithm manupulation of the adjacency matrix.","accessed":{"date-parts":[["2024",11,5]]},"author":[{"family":"Corso","given":"Gilberto"},{"family":"De Araujo","given":"A I Levartoski"},{"family":"De Almeida","given":"Adriana M"}],"citation-key":"corsoConnectivityNestednessBipartite2011a","container-title":"Journal of Physics: Conference Series","container-title-short":"J. Phys.: Conf. Ser.","DOI":"10.1088/1742-6596/285/1/012009","ISSN":"1742-6596","issued":{"date-parts":[["2011",3,1]]},"language":"en","page":"012009","source":"DOI.org (Crossref)","title":"Connectivity and Nestedness in Bipartite Networks from Community Ecology","type":"article-journal","URL":"https://iopscience.iop.org/article/10.1088/1742-6596/285/1/012009","volume":"285"},{"id":"coulomEfficientSelectivityBackup2007","abstract":"Monte-Carlo evaluation consists in estimating a position by averaging the outcome of several random continuations, and can serve as an evaluation function at the leaves of a min-max tree. This paper presents a new framework to combine tree search with Monte-Carlo evaluation, that does not separate between a min-max phase and a MonteCarlo phase. Instead of backing-up the min-max value close to the root, and the average value at some depth, a more general backup operator is defined that progressively changes from averaging to min-max as the number of simulations grows. This approach provides a fine-grained control of the tree growth, at the level of individual simulations, and allows efficient selectivity methods. This algorithm was implemented in a 9 × 9 Go-playing program, Crazy Stone, that won the 10th KGS computer-Go tournament.","accessed":{"date-parts":[["2024",2,9]]},"author":[{"family":"Coulom","given":"Rémi"}],"citation-key":"coulomEfficientSelectivityBackup2007","collection-editor":[{"family":"Hutchison","given":"David"},{"family":"Kanade","given":"Takeo"},{"family":"Kittler","given":"Josef"},{"family":"Kleinberg","given":"Jon M."},{"family":"Mattern","given":"Friedemann"},{"family":"Mitchell","given":"John C."},{"family":"Naor","given":"Moni"},{"family":"Nierstrasz","given":"Oscar"},{"family":"Pandu Rangan","given":"C."},{"family":"Steffen","given":"Bernhard"},{"family":"Sudan","given":"Madhu"},{"family":"Terzopoulos","given":"Demetri"},{"family":"Tygar","given":"Doug"},{"family":"Vardi","given":"Moshe Y."},{"family":"Weikum","given":"Gerhard"}],"collection-title":"Lecture Notes in Computer Science","container-title":"Computers and Games","DOI":"10.1007/978-3-540-75538-8_7","editor":[{"family":"Van Den Herik","given":"H. Jaap"},{"family":"Ciancarini","given":"Paolo"},{"family":"Donkers","given":"H. H. L. M."}],"ISBN":"978-3-540-75537-1 978-3-540-75538-8","issued":{"date-parts":[["2007"]]},"language":"en","page":"72–83","publisher":"Springer Berlin Heidelberg","publisher-place":"Berlin, Heidelberg","source":"DOI.org (Crossref)","title":"Efficient Selectivity and Backup Operators in Monte-Carlo Tree Search","type":"chapter","URL":"http://link.springer.com/10.1007/978-3-540-75538-8_7","volume":"4630"},{"id":"csilleryApproximateBayesianComputation","author":[{"family":"Csilléry","given":"K"},{"family":"Lemaire","given":"L"},{"family":"François","given":"O"},{"family":"Blum","given":"MGB"}],"citation-key":"csilleryApproximateBayesianComputation","language":"en","note":"Read_Status: New\nRead_Status_Date: 2026-05-05T09:10:00.661Z","source":"Zotero","title":"Approximate Bayesian Computation (ABC) in R: A Vignette","type":"article-journal"},{"id":"csilleryApproximateBayesianComputation2010","abstract":"Understanding the forces that influence natural variation within and among populations has been a major objective of evolutionary biologists for decades. Motivated by the growth in computational power and data complexity, modern approaches to this question make intensive use of simulation methods. Approximate Bayesian Computation (ABC) is one of these methods. Here we review the foundations of ABC, its recent algorithmic developments, and its applications in evolutionary biology and ecology. We argue that the use of ABC should incorporate all aspects of Bayesian data analysis: formulation, fitting, and improvement of a model. ABC can be a powerful tool to make inferences with complex models if these principles are carefully applied.","author":[{"family":"Csilléry","given":"Katalin"},{"family":"Blum","given":"Michael G. B."},{"family":"Gaggiotti","given":"Oscar E."},{"family":"François","given":"Olivier"}],"citation-key":"csilleryApproximateBayesianComputation2010","container-title":"Trends in Ecology & Evolution","container-title-short":"Trends Ecol Evol","DOI":"10.1016/j.tree.2010.04.001","ISSN":"0169-5347","issue":"7","issued":{"date-parts":[["2010",7]]},"language":"eng","note":"Read_Status: New\nRead_Status_Date: 2026-04-07T13:22:11.099Z","page":"410–418","PMID":"20488578","source":"PubMed","title":"Approximate Bayesian Computation (ABC) in practice","type":"article-journal","volume":"25"},{"id":"daigavaneUnderstandingConvolutionsGraphs2021","abstract":"Understanding the building blocks and design choices of graph neural networks.","accessed":{"date-parts":[["2024",5,21]]},"author":[{"family":"Daigavane","given":"Ameya"},{"family":"Ravindran","given":"Balaraman"},{"family":"Aggarwal","given":"Gaurav"}],"citation-key":"daigavaneUnderstandingConvolutionsGraphs2021","container-title":"Distill","container-title-short":"Distill","DOI":"10.23915/distill.00032","ISSN":"2476-0757","issue":"9","issued":{"date-parts":[["2021",9,2]]},"language":"en","page":"e32","source":"distill.pub","title":"Understanding Convolutions on Graphs","type":"article-journal","URL":"https://distill.pub/2021/understanding-gnns","volume":"6"},{"id":"daudinMixtureModelRandom2008","abstract":"The Erdös–Rényi model of a network is simple and possesses many explicit expressions for average and asymptotic properties, but it does not fit well to real-world networks. The vertices of those networks are often structured in unknown classes (functionally related proteins or social communities) with different connectivity properties. The stochastic block structures model was proposed for this purpose in the context of social sciences, using a Bayesian approach. We consider the same model in a frequentest statistical framework. We give the degree distribution and the clustering coefficient associated with this model, a variational method to estimate its parameters and a model selection criterion to select the number of classes. This estimation procedure allows us to deal with large networks containing thousands of vertices. The method is used to uncover the modular structure of a network of enzymatic reactions.","accessed":{"date-parts":[["2023",6,16]]},"author":[{"family":"Daudin","given":"J.-J."},{"family":"Picard","given":"F."},{"family":"Robin","given":"S."}],"citation-key":"daudinMixtureModelRandom2008","container-title":"Statistics and Computing","container-title-short":"Stat Comput","DOI":"10.1007/s11222-007-9046-7","ISSN":"1573-1375","issue":"2","issued":{"date-parts":[["2008",6,1]]},"language":"en","page":"173–183","source":"Springer Link","title":"A mixture model for random graphs","type":"article-journal","URL":"https://doi.org/10.1007/s11222-007-9046-7","volume":"18"},{"id":"daudinMixtureModelRandom2008","abstract":"The Erdös–Rényi model of a network is simple and possesses many explicit expressions for average and asymptotic properties, but it does not fit well to real-world networks. The vertices of those networks are often structured in unknown classes (functionally related proteins or social communities) with different connectivity properties. The stochastic block structures model was proposed for this purpose in the context of social sciences, using a Bayesian approach. We consider the same model in a frequentest statistical framework. We give the degree distribution and the clustering coefficient associated with this model, a variational method to estimate its parameters and a model selection criterion to select the number of classes. This estimation procedure allows us to deal with large networks containing thousands of vertices. The method is used to uncover the modular structure of a network of enzymatic reactions.","accessed":{"date-parts":[["2023",6,16]]},"author":[{"family":"Daudin","given":"J.-J."},{"family":"Picard","given":"F."},{"family":"Robin","given":"S."}],"citation-key":"daudinMixtureModelRandom2008","container-title":"Statistics and computing","container-title-short":"Stat Comput","DOI":"10.1007/s11222-007-9046-7","ISSN":"1573-1375","issue":"2","issued":{"date-parts":[["2008",6,1]]},"language":"english","page":"173–183","title":"A mixture model for random graphs","type":"article-journal","URL":"https://doi.org/10.1007/s11222-007-9046-7","volume":"18"},{"id":"daveziesAnalyticInferenceMultiway","author":[{"family":"Davezies","given":"Laurent"},{"family":"D'Haultfœuille","given":"Xavier"},{"family":"Guyonvarch","given":"Yannick"}],"citation-key":"daveziesAnalyticInferenceMultiway","language":"en","source":"Zotero","title":"Analytic inference with multiway clustering","type":"article-journal"},{"id":"DeepBlue2023","abstract":"Deep Blue est un superordinateur spécialisé dans le jeu d'échecs par adjonction de circuits spécifiques, développé par IBM au début des années 1990.\nPerdant un match en 1996 (2-4) contre le champion du monde d'échecs de l'époque Garry Kasparov, Deep Blue (surnommé alors Deeper Blue) bat le champion du monde (3,5–2,5) lors du match revanche en 1997, mais hors des conditions exigées lors des championnats du monde.","accessed":{"date-parts":[["2024",2,5]]},"citation-key":"DeepBlue2023","container-title":"Wikipédia","issued":{"date-parts":[["2023",11,20]]},"language":"fr","license":"Creative Commons Attribution-ShareAlike License","note":"Page Version ID: 209844071","source":"Wikipedia","title":"Deep Blue","type":"entry-encyclopedia","URL":"https://fr.wikipedia.org/w/index.php?title=Deep_Blue&oldid=209844071"},{"id":"degouletSpecificSensitivityRare2024","abstract":"Most studies assessing animal decision-making under risk rely on probabilities that are typically larger than 10%. To study Decision-Making in uncertain conditions, we explore a novel experimental and modelling approach that aims at measuring the extent to which rats are sensitive - and how they respond - to outcomes that are both rare (probabilities smaller than 1%) and extreme in their consequences (deviations larger than 10 times the standard error). In a four-armed bandit task, stochastic gains (sugar pellets) and losses (time-out punishments) are such that extremely large - but rare - outcomes materialize or not depending on the chosen options. All rats feature both limited diversification, mixing two options out of four, and sensitivity to rare and extreme outcomes despite their infrequent occurrence, by combining options with avoidance of extreme losses (Black Swans) and exposure to extreme gains (Jackpots). Notably, this sensitivity turns out to be one-sided for the main phenotype in our sample: it features a quasi-complete avoidance of Black Swans, so as to escape extreme losses almost completely, which contrasts with an exposure to Jackpots that is partial only. The flip side of observed choices is that they entail smaller gains and larger losses in the frequent domain compared to alternatives. We have introduced sensitivity to Black Swans and Jackpots in a new class of augmented Reinforcement Learning models and we have estimated their parameters using observed choices and outcomes for each rat. Adding such specific sensitivity results in a good fit of the selected model - and simulated behaviors that are close - to behavioral observations, whereas a standard Q-Learning model without sensitivity is rejected for almost all rats. This model reproducing the main phenotype suggests that frequent outcomes are treated separately from rare and extreme ones through different weights in Decision-Making.","accessed":{"date-parts":[["2024",11,4]]},"author":[{"family":"Degoulet","given":"Mickaël"},{"family":"Willem","given":"Louis-Matis"},{"family":"Baunez","given":"Christelle"},{"family":"Luchini","given":"Stéphane"},{"family":"Pintus","given":"Patrick A"}],"citation-key":"degouletSpecificSensitivityRare2024","DOI":"10.7554/eLife.98487.1","issued":{"date-parts":[["2024",7,26]]},"license":"http://creativecommons.org/licenses/by/4.0/","source":"DOI.org (Crossref)","title":"Specific Sensitivity to Rare and Extreme Events: Quasi-Complete Black Swan Avoidance vs Partial Jackpot Seeking in Rat Decision-Making","title-short":"Specific Sensitivity to Rare and Extreme Events","type":"article","URL":"https://elifesciences.org/reviewed-preprints/98487v1"},{"id":"delonWassersteintypeDistanceSpace2020","abstract":"In this paper we introduce a Wasserstein-type distance on the set of Gaussian mixture models. This distance is defined by restricting the set of possible coupling measures in the optimal transport problem to Gaussian mixture models. We derive a very simple discrete formulation for this distance, which makes it suitable for high dimensional problems. We also study the corresponding multimarginal and barycenter formulations. We show some properties of this Wasserstein-type distance, and we illustrate its practical use with some examples in image processing.","accessed":{"date-parts":[["2024",6,6]]},"author":[{"family":"Delon","given":"Julie"},{"family":"Desolneux","given":"Agnes"}],"citation-key":"delonWassersteintypeDistanceSpace2020","issued":{"date-parts":[["2020",6,11]]},"language":"en","number":"arXiv:1907.05254","publisher":"arXiv","source":"arXiv.org","title":"A Wasserstein-type distance in the space of Gaussian Mixture Models","type":"article","URL":"http://arxiv.org/abs/1907.05254"},{"id":"dempsterMaximumLikelihoodIncomplete1977","abstract":"A broadly applicable algorithm for computing maximum likelihood estimates from incomplete data is presented at various levels of generality. Theory showing the monotone behaviour of the likelihood and convergence of the algorithm is derived. Many examples are sketched, including missing value situations, applications to grouped, censored or truncated data, finite mixture models, variance component estimation, hyperparameter estimation, iteratively reweighted least squares and factor analysis.","accessed":{"date-parts":[["2025",5,27]]},"author":[{"family":"Dempster","given":"A. P."},{"family":"Laird","given":"N. M."},{"family":"Rubin","given":"D. B."}],"citation-key":"dempsterMaximumLikelihoodIncomplete1977","container-title":"Journal of the Royal Statistical Society. Series B (Methodological)","ISSN":"0035-9246","issue":"1","issued":{"date-parts":[["1977"]]},"note":"Read_Status: New\nRead_Status_Date: 2025-05-27T16:20:41.925Z","page":"1–38","publisher":"[Royal Statistical Society, Oxford University Press]","source":"JSTOR","title":"Maximum Likelihood from Incomplete Data via the EM Algorithm","type":"article-journal","URL":"https://www.jstor.org/stable/2984875","volume":"39"},{"id":"derrUsingNetworkDensity2024","abstract":"Learn how to assess network density to optimize collaboration, prevent overload, and strengthen connectivity using network analysis for strategic insights.","accessed":{"date-parts":[["2025",9,21]]},"author":[{"family":"Derr","given":"Alex"}],"citation-key":"derrUsingNetworkDensity2024","container-title":"Visible Network Labs","issued":{"date-parts":[["2024",11,13]]},"language":"en-US","note":"Read_Status: New\nRead_Status_Date: 2025-09-23T11:03:32.617Z","title":"Using Network Density to Evaluate and Optimize Collaboration Intensity","type":"post-weblog","URL":"https://visiblenetworklabs.com/2024/11/13/using-network-density-to-evaluate-and-optimize-collaboration-intensity/"},{"id":"desjardins-proulxEcologicalInteractionsNetflix2017","abstract":"Species interactions are a key component of ecosystems but we generally have an incomplete picture of who-eats-who in a given community. Different techniques have been devised to predict species interactions using theoretical models or abundances. Here, we explore the K nearest neighbour approach, with a special emphasis on recommendation, along with a supervised machine learning technique. Recommenders are algorithms developed for companies like Netflix to predict whether a customer will like a product given the preferences of similar customers. These machine learning techniques are well-suited to study binary ecological interactions since they focus on positive-only data. By removing a prey from a predator, we find that recommenders can guess the missing prey around 50% of the times on the first try, with up to 881 possibilities. Traits do not improve significantly the results for the K nearest neighbour, although a simple test with a supervised learning approach (random forests) show we can predict interactions with high accuracy using only three traits per species. This result shows that binary interactions can be predicted without regard to the ecological community given only three variables: body mass and two variables for the species’ phylogeny. These techniques are complementary, as recommenders can predict interactions in the absence of traits, using only information about other species’ interactions, while supervised learning algorithms such as random forests base their predictions on traits only but do not exploit other species’ interactions. Further work should focus on developing custom similarity measures specialized for ecology to improve the KNN algorithms and using richer data to capture indirect relationships between species.","accessed":{"date-parts":[["2023",6,15]]},"author":[{"family":"Desjardins-Proulx","given":"Philippe"},{"family":"Laigle","given":"Idaline"},{"family":"Poisot","given":"Timothée"},{"family":"Gravel","given":"Dominique"}],"citation-key":"desjardins-proulxEcologicalInteractionsNetflix2017","container-title":"PeerJ","container-title-short":"PeerJ","DOI":"10.7717/peerj.3644","ISSN":"2167-8359","issued":{"date-parts":[["2017",8,10]]},"language":"en","page":"e3644","publisher":"PeerJ Inc.","source":"peerj.com","title":"Ecological interactions and the Netflix problem","type":"article-journal","URL":"https://peerj.com/articles/3644","volume":"5"},{"id":"desmetAdvantagesLimitationsCurrent2010","abstract":"Recently several novel tools for inferring transcriptional networks from expression data have been developed. Computationally inferred interactions offer a useful resource to complement experimental findings, but the direct integration of inference tools in daily laboratory practice remains limited, because the choice of the appropriate network tool is not obvious.Network inference is, mathematically, an underdetermined problem. The large number of theoretically possible interactions between transcription factors (TFs) and their targets far exceeds the number of independent measurements from which the true interactions can be inferred. Inference therefore results in many possible solutions that all explain the data equally well, but only a few of these solutions can be biologically true.Different state-of-the-art tools for network inference deal with underdetermination by using assumptions and simplifications that reduce the number of possible solutions in order to make the problem solvable.The strategy adopted to deal with the inference problem determines the aspects of the transcriptional network that is highlighted and the type of research question that can be answered. The outcome of network inference therefore varies greatly between tools.Fair benchmark studies are useful for guiding both users and developers. Most current studies combine validation based on an external standard with medium-throughput experiments to validate the extent to which known interactions can be recovered and reliable new interactions can be inferred.It is likely that no single best method exists, and different methods highlight complementary interaction types. Therefore, ensemble approaches, which aggregate the outcomes of several methods, offer a way to improve on the breadth and the accuracy of the predicted interactions.Future work in the light of novel data generation procedures will be to develop inference methods that exploit high-throughput information about regulation at levels other than transcription to mechanistically explain how genomic variations result in observed expression changes.","accessed":{"date-parts":[["2024",5,16]]},"author":[{"family":"De Smet","given":"Riet"},{"family":"Marchal","given":"Kathleen"}],"citation-key":"desmetAdvantagesLimitationsCurrent2010","container-title":"Nature Reviews Microbiology","container-title-short":"Nat Rev Microbiol","DOI":"10.1038/nrmicro2419","ISSN":"1740-1534","issue":"10","issued":{"date-parts":[["2010",10]]},"language":"en","license":"2010 Springer Nature Limited","page":"717–729","publisher":"Nature Publishing Group","source":"www.nature.com","title":"Advantages and limitations of current network inference methods","type":"article-journal","URL":"https://www.nature.com/articles/nrmicro2419","volume":"8"},{"id":"devotoUnderstandingPlanningEcological2012","abstract":"Ecology Letters (2012) 15: 319–328 Abstract Theory developed from studying changes in the structure and function of communities during natural or managed succession can guide the restoration of particular communities. We constructed 30 quantitative plant–flower visitor networks along a managed successional gradient to identify the main drivers of change in network structure. We then applied two alternative restoration strategies in silico (restoring for functional complementarity or redundancy) to data from our early successional plots to examine whether different strategies affected the restoration trajectories. Changes in network structure were explained by a combination of age, tree density and variation in tree diameter, even when variance explained by undergrowth structure was accounted for first. A combination of field data, a network approach and numerical simulations helped to identify which species should be given restoration priority in the context of different restoration targets. This combined approach provides a powerful tool for directing management decisions, particularly when management seeks to restore or conserve ecosystem function.","accessed":{"date-parts":[["2024",8,20]]},"author":[{"family":"Devoto","given":"Mariano"},{"family":"Bailey","given":"Sallie"},{"family":"Craze","given":"Paul"},{"family":"Memmott","given":"Jane"}],"citation-key":"devotoUnderstandingPlanningEcological2012","container-title":"Ecology Letters","DOI":"10.1111/j.1461-0248.2012.01740.x","ISSN":"1461-0248","issue":"4","issued":{"date-parts":[["2012"]]},"language":"en","license":"© 2012 Blackwell Publishing Ltd/CNRS","page":"319–328","source":"Wiley Online Library","title":"Understanding and planning ecological restoration of plant–pollinator networks","type":"article-journal","URL":"https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1461-0248.2012.01740.x","volume":"15"},{"id":"dion-blancDetectionRupturesMultiples2023","abstract":"La détection des points de rupture a pour but de découvrir les changements de comportement qui se cachent derrière les données de séquences temporelles. Dans ce travail, nous étudions le cas où les données proviennent d'un processus de Poisson hétérogène d'intensité constante par morceaux. Nous présentons une méthodologie de détection, hors ligne, de points de changement multiples, basée sur l'estimateur de contraste minimum. En particulier, nous expliquons comment traiter la nature continue du processus. Enfin, nous sélectionnons le nombre de segments à l'aide d'une procédure de validation croisée, rendu possible ici par une propriété spécifique au processus de Poisson. La méthode proposée est implémentée dans le package R CptPointProcess.","accessed":{"date-parts":[["2024",1,31]]},"author":[{"family":"Dion-Blanc","given":"Charlotte"},{"family":"Lebarbier","given":"E."},{"family":"Robin","given":"Stephane S."}],"citation-key":"dion-blancDetectionRupturesMultiples2023","container-title":"54ème Journées De Statistique De Société Française De Statistique","issued":{"date-parts":[["2023",6]]},"publisher-place":"Bruxelles, Belgium","source":"HAL Archives Ouvertes","title":"Détection de ruptures multiples pour les processus de Poisson","type":"paper-conference","URL":"https://hal.science/hal-04403138"},{"id":"DivisiveAnalysisProgram1990","abstract":"The prelims comprise: Short Description of the Method How to Use the Program DIANA Examples More on the Algorithm and the Program Related Methods and References","accessed":{"date-parts":[["2024",9,13]]},"citation-key":"DivisiveAnalysisProgram1990","container-title":"Finding Groups in Data","DOI":"10.1002/9780470316801.ch6","ISBN":"978-0-470-31680-1","issued":{"date-parts":[["1990"]]},"language":"en","page":"253–279","publisher":"John Wiley & Sons, Ltd","section":"6","source":"Wiley Online Library","title":"Divisive Analysis (Program DIANA)","type":"chapter","URL":"https://onlinelibrary.wiley.com/doi/abs/10.1002/9780470316801.ch6"},{"id":"DocumentationUtilisateurForge","abstract":"Description will go into a meta tag in <head />","accessed":{"date-parts":[["2025",3,10]]},"citation-key":"DocumentationUtilisateurForge","language":"fr","title":"Documentation utilisateur de Forge INRAE | Documentation utilisateur de Forge INRAE","type":"webpage","URL":"https://forge-inrae.pages.mia.inra.fr/doc-utilisateur/"},{"id":"donnetLatentVariableModels","author":[{"family":"Donnet","given":"Sophie"}],"citation-key":"donnetLatentVariableModels","language":"en","source":"Zotero","title":"Latent variable models in biology and ecology - Chapter 4: Stochastic Block Models and Latent Block Models","type":"article-journal"},{"id":"doreRelativeEffectsAnthropogenic2021","abstract":"Pollinators provide crucial ecosystem services that underpin to wild plant reproduction and yields of insect-pollinated crops. Understanding the relative impacts of anthropogenic pressures and climate on the structure of plant–pollinator interaction networks is vital considering ongoing global change and pollinator decline. Our ability to predict the consequences of global change for pollinator assemblages worldwide requires global syntheses, but these analytical approaches may be hindered by variable methods among studies that either invalidate comparisons or mask biological phenomena. Here we conducted a synthetic analysis that assesses the relative impact of anthropogenic pressures and climatic variability, and accounts for heterogeneity in sampling methodology to reveal network responses at the global scale. We analyzed an extensive dataset, comprising 295 networks over 123 locations all over the world, and reporting over 50,000 interactions between flowering plant species and their insect visitors. Our study revealed that anthropogenic pressures correlate with an increase in generalism in pollination networks while pollinator richness and taxonomic composition are more related to climatic variables with an increase in dipteran pollinator richness associated with cooler temperatures. The contrasting response of species richness and generalism of the plant–pollinator networks stresses the importance of considering interaction network structure alongside diversity in ecological monitoring. In addition, differences in sampling design explained more variation than anthropogenic pressures or climate on both pollination networks richness and generalism, highlighting the crucial need to report and incorporate sampling design in macroecological comparative studies of pollination networks. As a whole, our study reveals a potential human impact on pollination networks at a global scale. However, further research is needed to evaluate potential consequences of loss of specialist species and their unique ecological interactions and evolutionary pathways on the ecosystem pollination function at a global scale.","accessed":{"date-parts":[["2023",6,21]]},"author":[{"family":"Doré","given":"Maël"},{"family":"Fontaine","given":"Colin"},{"family":"Thébault","given":"Elisa"}],"citation-key":"doreRelativeEffectsAnthropogenic2021","container-title":"Global Change Biology","DOI":"10.1111/gcb.15474","ISSN":"1365-2486","issue":"6","issued":{"date-parts":[["2021"]]},"language":"en","license":"© 2020 John Wiley & Sons Ltd","page":"1266–1280","source":"Wiley Online Library","title":"Relative effects of anthropogenic pressures, climate, and sampling design on the structure of pollination networks at the global scale","type":"article-journal","URL":"https://onlinelibrary.wiley.com/doi/abs/10.1111/gcb.15474","volume":"27"},{"id":"doreRelativeEffectsAnthropogenic2021","abstract":"Pollinators provide crucial ecosystem services that underpin to wild plant reproduction and yields of insect-pollinated crops. Understanding the relative impacts of anthropogenic pressures and climate on the structure of plant–pollinator interaction networks is vital considering ongoing global change and pollinator decline. Our ability to predict the consequences of global change for pollinator assemblages worldwide requires global syntheses, but these analytical approaches may be hindered by variable methods among studies that either invalidate comparisons or mask biological phenomena. Here we conducted a synthetic analysis that assesses the relative impact of anthropogenic pressures and climatic variability, and accounts for heterogeneity in sampling methodology to reveal network responses at the global scale. We analyzed an extensive dataset, comprising 295 networks over 123 locations all over the world, and reporting over 50,000 interactions between flowering plant species and their insect visitors. Our study revealed that anthropogenic pressures correlate with an increase in generalism in pollination networks while pollinator richness and taxonomic composition are more related to climatic variables with an increase in dipteran pollinator richness associated with cooler temperatures. The contrasting response of species richness and generalism of the plant–pollinator networks stresses the importance of considering interaction network structure alongside diversity in ecological monitoring. In addition, differences in sampling design explained more variation than anthropogenic pressures or climate on both pollination networks richness and generalism, highlighting the crucial need to report and incorporate sampling design in macroecological comparative studies of pollination networks. As a whole, our study reveals a potential human impact on pollination networks at a global scale. However, further research is needed to evaluate potential consequences of loss of specialist species and their unique ecological interactions and evolutionary pathways on the ecosystem pollination function at a global scale.","accessed":{"date-parts":[["2023",6,21]]},"author":[{"family":"Doré","given":"Maël"},{"family":"Fontaine","given":"Colin"},{"family":"Thébault","given":"Elisa"}],"citation-key":"doreRelativeEffectsAnthropogenic2021","container-title":"Global Change Biology","DOI":"10.1111/gcb.15474","ISSN":"1365-2486","issue":"6","issued":{"date-parts":[["2021"]]},"language":"en","license":"© 2020 John Wiley & Sons Ltd","note":"Read_Status: New\nRead_Status_Date: 2026-03-27T14:34:36.943Z","page":"1266–1280","source":"Wiley Online Library","title":"Relative effects of anthropogenic pressures, climate, and sampling design on the structure of pollination networks at the global scale","type":"article-journal","URL":"https://onlinelibrary.wiley.com/doi/abs/10.1111/gcb.15474","volume":"27"},{"id":"doreRelativeEffectsAnthropogenic2021","abstract":"Pollinators provide crucial ecosystem services that underpin to wild plant reproduction and yields of insect-pollinated crops. Understanding the relative impacts of anthropogenic pressures and climate on the structure of plant–pollinator interaction networks is vital considering ongoing global change and pollinator decline. Our ability to predict the consequences of global change for pollinator assemblages worldwide requires global syntheses, but these analytical approaches may be hindered by variable methods among studies that either invalidate comparisons or mask biological phenomena. Here we conducted a synthetic analysis that assesses the relative impact of anthropogenic pressures and climatic variability, and accounts for heterogeneity in sampling methodology to reveal network responses at the global scale. We analyzed an extensive dataset, comprising 295 networks over 123 locations all over the world, and reporting over 50,000 interactions between flowering plant species and their insect visitors. Our study revealed that anthropogenic pressures correlate with an increase in generalism in pollination networks while pollinator richness and taxonomic composition are more related to climatic variables with an increase in dipteran pollinator richness associated with cooler temperatures. The contrasting response of species richness and generalism of the plant–pollinator networks stresses the importance of considering interaction network structure alongside diversity in ecological monitoring. In addition, differences in sampling design explained more variation than anthropogenic pressures or climate on both pollination networks richness and generalism, highlighting the crucial need to report and incorporate sampling design in macroecological comparative studies of pollination networks. As a whole, our study reveals a potential human impact on pollination networks at a global scale. However, further research is needed to evaluate potential consequences of loss of specialist species and their unique ecological interactions and evolutionary pathways on the ecosystem pollination function at a global scale.","accessed":{"date-parts":[["2023",6,21]]},"author":[{"family":"Doré","given":"Maël"},{"family":"Fontaine","given":"Colin"},{"family":"Thébault","given":"Elisa"}],"citation-key":"doreRelativeEffectsAnthropogenic2021","container-title":"Global Change Biology","DOI":"10.1111/gcb.15474","ISSN":"1365-2486","issue":"6","issued":{"date-parts":[["2021"]]},"language":"english","note":"Read_Status: New\nRead_Status_Date: 2025-12-01T16:49:01.979Z","page":"1266–1280","title":"Relative effects of anthropogenic pressures, climate, and sampling design on the structure of pollination networks at the global scale","type":"article-journal","URL":"https://onlinelibrary.wiley.com/doi/abs/10.1111/gcb.15474","volume":"27"},{"id":"dormannIndicesGraphsNull2009","abstract":"Many analyses of ecological networks in recent years have introduced new indices to describe network properties. As a consequence, tens of indices are available to address similar questions, differing in specific detail, sensitivity in detecting the property in question, and robustness with respect to network size and sampling intensity. Furthermore, some indices merely reflect the number of species participating in a network, but not their interrelationship, requiring a null model approach. Here we introduce a new, free software calculating a large spectrum of network indices, visualizing bipartite networks and generating null models. We use this tool to explore the sensitivity of 26 network indices to network dimensions, sampling intensity and singleton observations. Based on observed data, we investigate the interrelationship of these indices, and show that they are highly correlated, and heavily influenced by network dimensions and connectance. Finally, we re-evaluate five common hypotheses about network properties, comparing 19 pollination networks with three differently complex null models: 1. The number of links per species (“degree”) follow (truncated) power law distributions. 2. Generalist pollinators interact with specialist plants, and vice versa (dependence asymmetry). 3. Ecological networks are nested. 4. Pollinators display complementarity, owing to specialization within the network. 5. Plant-pollinator networks are more robust to extinction than random networks. Our results indicate that while some hypotheses hold up against our null models, others are to a large extent understandable on the basis of network size, rather than ecological interrelationships. In particular, null model pattern of dependence asymmetry and robustness to extinction are opposite to what current network paradigms suggest. Our analysis, and the tools we provide, enables ecologists to readily contrast their findings with null model expectations for many different questions, thus separating statistical inevitability from ecological process.","accessed":{"date-parts":[["2025",9,18]]},"author":[{"family":"Dormann","given":"Carsten F."},{"family":"Frund","given":"Jochen"},{"family":"Bluthgen","given":"Nico"},{"family":"Gruber","given":"Bernd"}],"citation-key":"dormannIndicesGraphsNull2009","container-title":"The Open Ecology Journal","container-title-short":"TOECOLJ","DOI":"10.2174/1874213000902010007","ISSN":"18742130","issue":"1","issued":{"date-parts":[["2009",2,27]]},"language":"en","note":"Read_Status: New\nRead_Status_Date: 2025-09-18T13:47:50.536Z","page":"7–24","source":"DOI.org (Crossref)","title":"Indices, Graphs and Null Models: Analyzing Bipartite Ecological Networks","title-short":"Indices, Graphs and Null Models","type":"article-journal","URL":"http://benthamopen.com/ABSTRACT/TOECOLJ-2-1-7","volume":"2"},{"id":"EconometricAnalysisCount2008","accessed":{"date-parts":[["2025",10,15]]},"citation-key":"EconometricAnalysisCount2008","DOI":"10.1007/978-3-540-78389-3","ISBN":"978-3-540-77648-2 978-3-540-78389-3","issued":{"date-parts":[["2008"]]},"language":"en","license":"http://www.springer.com/tdm","note":"Read_Status: New\nRead_Status_Date: 2025-10-15T07:40:26.245Z","publisher":"Springer Berlin Heidelberg","publisher-place":"Berlin, Heidelberg","source":"DOI.org (Crossref)","title":"Econometric Analysis of Count Data","type":"book","URL":"http://link.springer.com/10.1007/978-3-540-78389-3"},{"id":"elleUsePollinationNetworks2012","abstract":"Recent concern about declines in pollinating insects highlights the need for better understanding of plant–pollinator interactions. One promising approach at the community scale is network analysis, which allows actual interactions to be assessed, unlike biodiversity surveys, which only identify the potentially interacting organisms. We highlight useful network properties for conservation research and examples of their use in the study of rare species, invasive species, responses of communities to climate change, and habitat loss and restoration. We suggest that nestedness, degree, and interaction strength asymmetry are the most useful network properties for applied research on plant–pollinator interactions, but also highlight practical concerns regarding their measurement. We encourage the adoption of a network approach when an understanding of function within communities, rather than simple community composition, is useful for management.","accessed":{"date-parts":[["2025",9,18]]},"author":[{"family":"Elle","given":"Elizabeth"},{"family":"Elwell","given":"Sherri L."},{"family":"Gielens","given":"Grahame A."}],"citation-key":"elleUsePollinationNetworks2012","container-title":"Botany","container-title-short":"Botany","DOI":"10.1139/b11-111","ISSN":"1916-2790, 1916-2804","issue":"7","issued":{"date-parts":[["2012",7]]},"language":"en","license":"http://www.nrcresearchpress.com/page/about/CorporateTextAndDataMining","note":"Read_Status: New\nRead_Status_Date: 2025-09-18T15:39:13.968Z","page":"525–534","source":"DOI.org (Crossref)","title":"The use of pollination networks in conservation<sup>1</sup> This article is part of a Special Issue entitled “Pollination biology research in Canada: Perspectives on a mutualism at different scales”.","title-short":"The use of pollination networks in conservation<sup>1</sup> This article is part of a Special Issue entitled “Pollination biology research in Canada","type":"article-journal","URL":"http://www.nrcresearchpress.com/doi/10.1139/b11-111","volume":"90"},{"id":"elleUsePollinationNetworks2012","abstract":"Recent concern about declines in pollinating insects highlights the need for better understanding of plant–pollinator interactions. One promising approach at the community scale is network analysis, which allows actual interactions to be assessed, unlike biodiversity surveys, which only identify the potentially interacting organisms. We highlight useful network properties for conservation research and examples of their use in the study of rare species, invasive species, responses of communities to climate change, and habitat loss and restoration. We suggest that nestedness, degree, and interaction strength asymmetry are the most useful network properties for applied research on plant–pollinator interactions, but also highlight practical concerns regarding their measurement. We encourage the adoption of a network approach when an understanding of function within communities, rather than simple community composition, is useful for management.","accessed":{"date-parts":[["2025",9,18]]},"author":[{"family":"Elle","given":"Elizabeth"},{"family":"Elwell","given":"Sherri L."},{"family":"Gielens","given":"Grahame A."}],"citation-key":"elleUsePollinationNetworks2012","container-title":"Botany-botanique","container-title-short":"Botany","DOI":"10.1139/b11-111","ISSN":"1916-2790, 1916-2804","issue":"7","issued":{"date-parts":[["2012",7]]},"language":"english","page":"525–534","title":"The use of pollination networks in conservation¹ This article is part of a Special Issue entitled “Pollination biology research in Canada: Perspectives on a mutualism at different scales”.","title-short":"The use of pollination networks in conservation¹ This article is part of a Special Issue entitled “Pollination biology research in Canada","type":"article-journal","URL":"http://www.nrcresearchpress.com/doi/10.1139/b11-111","volume":"90"},{"id":"erdosRandomGraphs1959","abstract":"Semantic Scholar extracted view of \"On random graphs. I.\" by P. Erdos et al.","accessed":{"date-parts":[["2024",8,9]]},"author":[{"family":"Erdős","given":"P."},{"family":"Rényi","given":"A."}],"citation-key":"erdosRandomGraphs1959","container-title":"Publicationes Mathematicae Debrecen","container-title-short":"Publ. Math. Debrecen","DOI":"10.5486/PMD.1959.6.3-4.12","ISSN":"00333883","issue":"3–4","issued":{"date-parts":[["1959"]]},"page":"290–297","source":"Semantic Scholar","title":"On random graphs. I.","type":"article-journal","URL":"https://publi.math.unideb.hu/load_doi.php?pdoi=10_5486_PMD_1959_6_3_4_12","volume":"6"},{"id":"EvaluationFunction2024","abstract":"An evaluation function, also known as a heuristic evaluation function or static evaluation function, is a function used by game-playing computer programs to estimate the value or goodness of a position (usually at a leaf or terminal node) in a game tree. Most of the time, the value is either a real number or a quantized integer, often in nths of the value of a playing piece such as a stone in go or a pawn in chess, where n may be tenths, hundredths or other convenient fraction, but sometimes, the value is an array of three values in the unit interval, representing the win, draw, and loss percentages of the position. \nThere do not exist analytical or theoretical models for evaluation functions for unsolved games, nor are such functions entirely ad-hoc. The composition of evaluation functions is determined empirically by inserting a candidate function into an automaton and evaluating its subsequent performance. A significant body of evidence now exists for several games like chess, shogi and go as to the general composition of evaluation functions for them.\nGames in which game playing computer programs employ evaluation functions include chess, go, shogi (Japanese chess), othello, hex, backgammon, and checkers. In addition, with the advent of programs such as MuZero, computer programs also use evaluation functions to play video games, such as those from the Atari 2600. Some games like tic-tac-toe are strongly solved, and do not require search or evaluation because a discrete solution tree is available.","accessed":{"date-parts":[["2024",2,5]]},"citation-key":"EvaluationFunction2024","container-title":"Wikipedia","issued":{"date-parts":[["2024",2,1]]},"language":"en","license":"Creative Commons Attribution-ShareAlike License","note":"Page Version ID: 1201590101","source":"Wikipedia","title":"Evaluation function","type":"entry-encyclopedia","URL":"https://en.wikipedia.org/w/index.php?title=Evaluation_function&oldid=1201590101"},{"id":"faustOpenChallengesMicrobial2021","abstract":"Microbial network construction is a popular explorative data analysis technique in microbiome research. Although a large number of microbial network construction tools has been developed to date, there are several issues concerning the construction and interpretation of microbial networks that have received less attention. The purpose of this perspective is to draw attention to these underexplored challenges of microbial network construction and analysis.","accessed":{"date-parts":[["2025",5,5]]},"author":[{"family":"Faust","given":"Karoline"}],"citation-key":"faustOpenChallengesMicrobial2021","container-title":"The ISME Journal","container-title-short":"The ISME Journal","DOI":"10.1038/s41396-021-01027-4","ISSN":"1751-7362","issue":"11","issued":{"date-parts":[["2021",11,1]]},"note":"Read_Status: New\nRead_Status_Date: 2025-05-05T07:37:03.250Z","page":"3111–3118","source":"Silverchair","title":"Open challenges for microbial network construction and analysis","type":"article-journal","URL":"https://doi.org/10.1038/s41396-021-01027-4","volume":"15"},{"id":"fawziDiscoveringFasterMatrix2022","abstract":"Improving the efficiency of algorithms for fundamental computations can have a widespread impact, as it can affect the overall speed of a large amount of computations. Matrix multiplication is one such primitive task, occurring in many systems—from neural networks to scientific computing routines. The automatic discovery of algorithms using machine learning offers the prospect of reaching beyond human intuition and outperforming the current best human-designed algorithms. However, automating the algorithm discovery procedure is intricate, as the space of possible algorithms is enormous. Here we report a deep reinforcement learning approach based on AlphaZero1 for discovering efficient and provably correct algorithms for the multiplication of arbitrary matrices. Our agent, AlphaTensor, is trained to play a single-player game where the objective is finding tensor decompositions within a finite factor space. AlphaTensor discovered algorithms that outperform the state-of-the-art complexity for many matrix sizes. Particularly relevant is the case of 4 × 4 matrices in a finite field, where AlphaTensor’s algorithm improves on Strassen’s two-level algorithm for the first time, to our knowledge, since its discovery 50 years ago2. We further showcase the flexibility of AlphaTensor through different use-cases: algorithms with state-of-the-art complexity for structured matrix multiplication and improved practical efficiency by optimizing matrix multiplication for runtime on specific hardware. Our results highlight AlphaTensor’s ability to accelerate the process of algorithmic discovery on a range of problems, and to optimize for different criteria.","accessed":{"date-parts":[["2024",2,19]]},"author":[{"family":"Fawzi","given":"Alhussein"},{"family":"Balog","given":"Matej"},{"family":"Huang","given":"Aja"},{"family":"Hubert","given":"Thomas"},{"family":"Romera-Paredes","given":"Bernardino"},{"family":"Barekatain","given":"Mohammadamin"},{"family":"Novikov","given":"Alexander"},{"family":"R. Ruiz","given":"Francisco J."},{"family":"Schrittwieser","given":"Julian"},{"family":"Swirszcz","given":"Grzegorz"},{"family":"Silver","given":"David"},{"family":"Hassabis","given":"Demis"},{"family":"Kohli","given":"Pushmeet"}],"citation-key":"fawziDiscoveringFasterMatrix2022","container-title":"Nature","DOI":"10.1038/s41586-022-05172-4","ISSN":"1476-4687","issue":"7930","issued":{"date-parts":[["2022",10]]},"language":"en","license":"2022 The Author(s)","number":"7930","page":"47–53","publisher":"Nature Publishing Group","source":"www.nature.com","title":"Discovering faster matrix multiplication algorithms with reinforcement learning","type":"article-journal","URL":"https://www.nature.com/articles/s41586-%20022-05172-4","volume":"610"},{"id":"fisherTestingIdentityCovariance2012","abstract":"This article explores the problem of testing the hypothesis that the covariance matrix is an identity matrix when the dimensionality is equal to the sample size or larger. Two new test statistics are proposed under comparable assumptions to those statistics in the literature. The asymptotic distribution of the proposed test statistics are found and are shown to be consistent in the general asymptotic framework. An extensive simulation study shows the newly proposed tests are comparable to, and in some cases more powerful than, the tests for an identity covariance matrix currently in the literature.","accessed":{"date-parts":[["2024",2,4]]},"author":[{"family":"Fisher","given":"Thomas J."}],"citation-key":"fisherTestingIdentityCovariance2012","container-title":"Journal of Statistical Planning and Inference","container-title-short":"Journal of Statistical Planning and Inference","DOI":"10.1016/j.jspi.2011.07.019","ISSN":"0378-3758","issue":"1","issued":{"date-parts":[["2012",1,1]]},"page":"312–326","source":"ScienceDirect","title":"On testing for an identity covariance matrix when the dimensionality equals or exceeds the sample size","type":"article-journal","URL":"https://www.sciencedirect.com/science/article/pii/S0378375811003041","volume":"142"},{"id":"fisogniSeasonalTrajectoriesPlantpollinator2022","abstract":"Urbanization may significantly alter the abundance, composition and phenology of natural communities of plants and pollinators. However, how such alterations eventually affect the structure of plant-pollinator interaction networks is still poorly known. Here, we investigate how the structure of plant-pollinator networks changes along an urbanization gradient, which coincides with a phenological mismatch between plants and pollinators. We examined changes in plant-pollinator network structure in 12 sites sown with standardized native flower mixes along an urbanization gradient in a metropolis in Northern France. We used network-level metrics in combination with more detailed methodologies to identify changes in network structure, species clustering, and species roles through urban classes and time. We also evaluated the temporal trajectories of α- and β-diversity of species and interactions along the gradient. Network-level metrics showed limited spatial–temporal variability in the connectance, distribution of interactions and network-level specialization. Finer-scale analyses showed that generalist plant and pollinator species with long phenology were the most central and played key roles in defining the composition of cohesive groups of interacting species in all networks. Network motifs and species positions showed higher temporal variability in less urbanized areas, and interactions were more dissimilar between urbanization classes earlier in the season. We showed evidence of alterations in plant-pollinator network structure across space and time along an urbanization gradient, likely driven by the significant advancement in flowering phenology observed in the more urbanized areas. Our results emphasize the importance of targeted measures to maintain functional plant-pollinator communities, especially early in the season in highly urbanized areas.","accessed":{"date-parts":[["2025",5,14]]},"author":[{"family":"Fisogni","given":"Alessandro"},{"family":"Hautekèete","given":"Nina"},{"family":"Piquot","given":"Yves"},{"family":"Brun","given":"Marion"},{"family":"Vanappelghem","given":"Cédric"},{"family":"Ohlmann","given":"Marc"},{"family":"Franchomme","given":"Magalie"},{"family":"Hinnewinkel","given":"Christelle"},{"family":"Massol","given":"François"}],"citation-key":"fisogniSeasonalTrajectoriesPlantpollinator2022","container-title":"Landscape and Urban Planning","container-title-short":"Landscape and Urban Planning","DOI":"10.1016/j.landurbplan.2022.104512","ISSN":"0169-2046","issued":{"date-parts":[["2022",10,1]]},"note":"Read_Status: New\nRead_Status_Date: 2025-05-14T20:18:00.025Z","page":"104512","source":"ScienceDirect","title":"Seasonal trajectories of plant-pollinator interaction networks differ following phenological mismatches along an urbanization gradient","type":"article-journal","URL":"https://www.sciencedirect.com/science/article/pii/S016920462200161X","volume":"226"},{"id":"flamaryPOTPythonOptimal2025","abstract":"POT : Python Optimal Transport","accessed":{"date-parts":[["2025",1,28]]},"author":[{"family":"Flamary","given":"Rémi"},{"family":"Vincent-Cuaz","given":"Cédric"},{"family":"Courty","given":"Nicolas"},{"family":"Gramfort","given":"Alexandre"},{"family":"Kachaiev","given":"Oleksii"},{"family":"Quang Tran","given":"Huy"},{"family":"David","given":"Laurène"},{"family":"Bonet","given":"Clément"},{"family":"Cassereau","given":"Nathan"},{"family":"Gnassounou","given":"Théo"},{"family":"Tanguy","given":"Eloi"},{"family":"Delon","given":"Julie"},{"family":"Collas","given":"Antoine"},{"family":"Mazelet","given":"Sonia"},{"family":"Chapel","given":"Laetitia"},{"family":"Kerdoncuff","given":"Tanguy"},{"family":"Yu","given":"Xizheng"},{"family":"Feickert","given":"Matthew"},{"family":"Krzakala","given":"Paul"},{"family":"Liu","given":"Tianlin"},{"family":"Fernandes Montesuma","given":"Eduardo"}],"citation-key":"flamaryPOTPythonOptimal2025","genre":"Python","issued":{"date-parts":[["2025",1,27]]},"license":"MIT","original-date":{"date-parts":[["2016",10,20]]},"source":"GitHub","title":"POT Python Optimal Transport","type":"software","URL":"https://github.com/PythonOT/POT","version":"0.9.5"},{"id":"fortes-limaComplexGeneticAdmixture2021","abstract":"Admixture is a fundamental evolutionary process that has influenced genetic patterns in numerous species. Maximum-likelihood approaches based on allele frequencies and linkage-disequilibrium have been extensively used to infer admixture processes from genome-wide data sets, mostly in human populations. Nevertheless, complex admixture histories, beyond one or two pulses of admixture, remain methodologically challenging to reconstruct. We developed an Approximate Bayesian Computation (ABC) framework to reconstruct highly complex admixture histories from independent genetic markers. We built the software package MetHis to simulate independent SNPs or microsatellites in a two-way admixed population for scenarios with multiple admixture pulses, monotonically decreasing or increasing recurring admixture, or combinations of these scenarios. MetHis allows users to draw model-parameter values from prior distributions set by the user, and, for each simulation, MetHis can calculate numerous summary statistics describing genetic diversity patterns and moments of the distribution of individual admixture fractions. We coupled MetHis with existing machine-learning ABC algorithms and investigated the admixture history of admixed populations. Results showed that random forest ABC scenario-choice could accurately distinguish among most complex admixture scenarios, and errors were mainly found in regions of the parameter space where scenarios were highly nested, and, thus, biologically similar. We focused on African American and Barbadian populations as two study-cases. We found that neural network ABC posterior parameter estimation was accurate and reasonably conservative under complex admixture scenarios. For both admixed populations, we found that monotonically decreasing contributions over time, from Europe and Africa, explained the observed data more accurately than multiple admixture pulses. This approach will allow for reconstructing detailed admixture histories when maximum-likelihood methods are intractable.","author":[{"family":"Fortes-Lima","given":"Cesar A."},{"family":"Laurent","given":"Romain"},{"family":"Thouzeau","given":"Valentin"},{"family":"Toupance","given":"Bruno"},{"family":"Verdu","given":"Paul"}],"citation-key":"fortes-limaComplexGeneticAdmixture2021","container-title":"Molecular Ecology Resources","container-title-short":"Mol Ecol Resour","DOI":"10.1111/1755-0998.13325","ISSN":"1755-0998","issue":"4","issued":{"date-parts":[["2021",5]]},"language":"eng","note":"Read_Status: New\nRead_Status_Date: 2026-04-07T13:23:36.649Z","page":"1098–1117","PMCID":"PMC8247995","PMID":"33452723","source":"PubMed","title":"Complex genetic admixture histories reconstructed with Approximate Bayesian Computation","type":"article-journal","volume":"21"},{"id":"fosdickMultiresolutionNetworkModels2019","accessed":{"date-parts":[["2026",1,23]]},"author":[{"family":"Fosdick","given":"Bailey K."},{"family":"McCormick","given":"Tyler H."},{"family":"Murphy","given":"Thomas Brendan"},{"family":"Ng","given":"Tin Lok James"},{"family":"Westling","given":"Ted"}],"citation-key":"fosdickMultiresolutionNetworkModels2019","container-title":"Journal of Computational and Graphical Statistics","container-title-short":"Journal of Computational and Graphical Statistics","DOI":"10.1080/10618600.2018.1505633","ISSN":"1061-8600, 1537-2715","issue":"1","issued":{"date-parts":[["2019",1,2]]},"language":"en","note":"Read_Status: New\nRead_Status_Date: 2026-01-23T12:38:22.053Z","page":"185–196","source":"DOI.org (Crossref)","title":"Multiresolution Network Models","type":"article-journal","URL":"https://www.tandfonline.com/doi/full/10.1080/10618600.2018.1505633","volume":"28"},{"id":"friedmanInferringCorrelationNetworks2012","accessed":{"date-parts":[["2025",10,6]]},"author":[{"family":"Friedman","given":"Jonathan"},{"family":"Alm","given":"Eric J."}],"citation-key":"friedmanInferringCorrelationNetworks2012","container-title":"PLoS Computational Biology","container-title-short":"PLoS Comput Biol","DOI":"10.1371/journal.pcbi.1002687","editor":[{"family":"Von Mering","given":"Christian"}],"ISSN":"1553-7358","issue":"9","issued":{"date-parts":[["2012",9,20]]},"language":"en","note":"Read_Status: New\nRead_Status_Date: 2025-10-06T15:06:43.620Z","page":"e1002687","source":"DOI.org (Crossref)","title":"Inferring Correlation Networks from Genomic Survey Data","type":"article-journal","URL":"https://dx.plos.org/10.1371/journal.pcbi.1002687","volume":"8"},{"id":"Frontmatter1990","abstract":"The prelims comprise: Half Title Title Copyright Preface Contents","accessed":{"date-parts":[["2024",9,13]]},"citation-key":"Frontmatter1990","container-title":"Finding Groups in Data","DOI":"10.1002/9780470316801.fmatter","ISBN":"978-0-470-31680-1","issued":{"date-parts":[["1990"]]},"language":"en","page":"i-xiv","publisher":"John Wiley & Sons, Ltd","source":"Wiley Online Library","title":"Frontmatter","type":"chapter","URL":"https://onlinelibrary.wiley.com/doi/abs/10.1002/9780470316801.fmatter"},{"id":"funkeStochasticBlockModels2019","abstract":"Finding communities in complex networks is a challenging task and one promising approach is the Stochastic Block Model (SBM). But the influences from various fields led to a diversity of variants and inference methods. Therefore, a comparison of the existing techniques and an independent analysis of their capabilities and weaknesses is needed. As a first step, we review the development of different SBM variants such as the degree-corrected SBM of Karrer and Newman or Peixoto’s hierarchical SBM. Beside stating all these variants in a uniform notation, we show the reasons for their development. Knowing the variants, we discuss a variety of approaches to infer the optimal partition like the Metropolis-Hastings algorithm. We perform our analysis based on our extension of the Girvan-Newman test and the Lancichinetti-Fortunato-Radicchi benchmark as well as a selection of some real world networks. Using these results, we give some guidance to the challenging task of selecting an inference method and SBM variant. In addition, we give a simple heuristic to determine the number of steps for the Metropolis-Hastings algorithms that lack a usual stop criterion. With our comparison, we hope to guide researches in the field of SBM and highlight the problem of existing techniques to focus future research. Finally, by making our code freely available, we want to promote a faster development, integration and exchange of new ideas.","accessed":{"date-parts":[["2025",1,26]]},"author":[{"family":"Funke","given":"Thorben"},{"family":"Becker","given":"Till"}],"citation-key":"funkeStochasticBlockModels2019","container-title":"PLOS ONE","container-title-short":"PLOS ONE","DOI":"10.1371/journal.pone.0215296","ISSN":"1932-6203","issue":"4","issued":{"date-parts":[["2019",4,23]]},"language":"en","page":"e0215296","publisher":"Public Library of Science","source":"PLoS Journals","title":"Stochastic block models: A comparison of variants and inference methods","title-short":"Stochastic block models","type":"article-journal","URL":"https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0215296","volume":"14"},{"id":"FuzzyAnalysisProgram1990","abstract":"The prelims comprise: The Purpose of Fuzzy Clustering How to Use the Program FANNY Examples More on the Algorithm and the Program Related Methods and References","accessed":{"date-parts":[["2024",9,13]]},"citation-key":"FuzzyAnalysisProgram1990","container-title":"Finding Groups in Data","DOI":"10.1002/9780470316801.ch4","ISBN":"978-0-470-31680-1","issued":{"date-parts":[["1990"]]},"language":"en","page":"164–198","publisher":"John Wiley & Sons, Ltd","section":"4","source":"Wiley Online Library","title":"Fuzzy Analysis (Program FANNY)","type":"chapter","URL":"https://onlinelibrary.wiley.com/doi/abs/10.1002/9780470316801.ch4"},{"id":"gallagherSpectralEmbeddingWeighted2024","abstract":"When analyzing weighted networks using spectral embedding, a judicious transformation of the edge weights may produce better results. To formalize this idea, we consider the asymptotic behavior of spectral embedding for different edge-weight representations, under a generic low rank model. We measure the quality of different embeddings—which can be on entirely different scales—by how easy it is to distinguish communities, in an information-theoretical sense. For common types of weighted graphs, such as count networks or p-value networks, we find that transformations such as tempering or thresholding can be highly beneficial, both in theory and in practice. Supplementary materials for this article are available online.","accessed":{"date-parts":[["2026",1,12]]},"author":[{"family":"Gallagher","given":"Ian"},{"family":"Jones","given":"Andrew"},{"family":"Bertiger","given":"Anna"},{"family":"Priebe","given":"Carey E."},{"family":"Rubin-Delanchy","given":"Patrick"}],"citation-key":"gallagherSpectralEmbeddingWeighted2024","container-title":"Journal of the American Statistical Association","DOI":"10.1080/01621459.2023.2225239","ISSN":"0162-1459","issue":"547","issued":{"date-parts":[["2024",7,2]]},"note":"Read_Status: New\nRead_Status_Date: 2026-01-12T15:12:12.952Z","page":"1923–1932","publisher":"Taylor & Francis","source":"Taylor and Francis+NEJM","title":"Spectral Embedding of Weighted Graphs","type":"article-journal","URL":"https://doi.org/10.1080/01621459.2023.2225239","volume":"119"},{"id":"gantzHighlyEfficientCas9mediated2015","abstract":"Genetic engineering technologies can be used both to create transgenic mosquitoes carrying antipathogen effector genes targeting human malaria parasites and to generate gene-drive systems capable of introgressing the genes throughout wild vector populations. We developed a highly effective autonomous Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-associated protein 9 (Cas9)-mediated gene-drive system in the Asian malaria vector Anopheles stephensi, adapted from the mutagenic chain reaction (MCR). This specific system results in progeny of males and females derived from transgenic males exhibiting a high frequency of germ-line gene conversion consistent with homology-directed repair (HDR). This system copies an ∼17-kb construct from its site of insertion to its homologous chromosome in a faithful, site-specific manner. Dual anti-Plasmodium falciparum effector genes, a marker gene, and the autonomous gene-drive components are introgressed into ∼99.5% of the progeny following outcrosses of transgenic lines to wild-type mosquitoes. The effector genes remain transcriptionally inducible upon blood feeding. In contrast to the efficient conversion in individuals expressing Cas9 only in the germ line, males and females derived from transgenic females, which are expected to have drive component molecules in the egg, produce progeny with a high frequency of mutations in the targeted genome sequence, resulting in near-Mendelian inheritance ratios of the transgene. Such mutant alleles result presumably from nonhomologous end-joining (NHEJ) events before the segregation of somatic and germ-line lineages early in development. These data support the design of this system to be active strictly within the germ line. Strains based on this technology could sustain control and elimination as part of the malaria eradication agenda.","accessed":{"date-parts":[["2024",9,4]]},"author":[{"family":"Gantz","given":"Valentino M."},{"family":"Jasinskiene","given":"Nijole"},{"family":"Tatarenkova","given":"Olga"},{"family":"Fazekas","given":"Aniko"},{"family":"Macias","given":"Vanessa M."},{"family":"Bier","given":"Ethan"},{"family":"James","given":"Anthony A."}],"citation-key":"gantzHighlyEfficientCas9mediated2015","container-title":"Proceedings of the National Academy of Sciences","DOI":"10.1073/pnas.1521077112","issue":"49","issued":{"date-parts":[["2015",12,8]]},"page":"E6736-E6743","publisher":"Proceedings of the National Academy of Sciences","source":"pnas.org (Atypon)","title":"Highly efficient Cas9-mediated gene drive for population modification of the malaria vector mosquito Anopheles stephensi","type":"article-journal","URL":"https://www.pnas.org/doi/10.1073/pnas.1521077112","volume":"112"},{"id":"gantzMutagenicChainReaction2015","abstract":"Generating homozygous mutations\n Loss-of-function mutations may only produce a mutant phenotype when both copies of the gene are mutated. Gantz and Bier developed a method they call mutagenic chain reaction (MCR) that autocatalytically produces homozygous mutations. MCR uses the initial mutated allele to cause a mutation in the allele on the opposing chromosome and thus the homozygosity of the trait. MCR technology could have broad applications in diverse organisms.\n \n Science\n , this issue p. 442\n \n , \n A mutagenesis strategy autocatalytically converts mutations to the homozygous condition in fly somatic and germline cells.\n , \n \n An organism with a single recessive loss-of-function allele will typically have a wild-type phenotype, whereas individuals homozygous for two copies of the allele will display a mutant phenotype. We have developed a method called the mutagenic chain reaction (MCR), which is based on the CRISPR/Cas9 genome-editing system for generating autocatalytic mutations, to produce homozygous loss-of-function mutations. In\n Drosophila\n , we found that MCR mutations efficiently spread from their chromosome of origin to the homologous chromosome, thereby converting heterozygous mutations to homozygosity in the vast majority of somatic and germline cells. MCR technology should have broad applications in diverse organisms.","accessed":{"date-parts":[["2024",10,3]]},"author":[{"family":"Gantz","given":"Valentino M."},{"family":"Bier","given":"Ethan"}],"citation-key":"gantzMutagenicChainReaction2015","container-title":"Science","container-title-short":"Science","DOI":"10.1126/science.aaa5945","ISSN":"0036-8075, 1095-9203","issue":"6233","issued":{"date-parts":[["2015",4,24]]},"language":"en","license":"http://www.sciencemag.org/about/science-licenses-journal-article-reuse","page":"442–444","source":"DOI.org (Crossref)","title":"The mutagenic chain reaction: A method for converting heterozygous to homozygous mutations","title-short":"The mutagenic chain reaction","type":"article-journal","URL":"https://www.science.org/doi/10.1126/science.aaa5945","volume":"348"},{"id":"garcia-callejasEcologicalNetworksInteraction2024","abstract":"Understanding how the structure of ecological communities varies across biotic and abiotic dimensions is a fundamental goal in ecology. This challenge is now approachable due to the increasing availability of data on community structure across the globe. Ecological communities are often defined with respect to the guilds considered and the interactions they engage in, but it is unclear whether interactions of different types respond similarly to large-scale environmental gradients. Therefore, we don't know whether there exist differences in how the emergent structure of ecological networks varies across biogeographical gradients, depending on their constituent interaction types. Here, using a unique dataset of 952 networks across the globe, we provide a first comparison of network structural metrics and their large-scale variability for five overarching interaction types (feeding, frugivory, herbivory, parasitism, pollination). We show that networks of different types tend to be more modular than expected, but other structural metrics do not deviate from what is expected given the degree distributions of the networks. Our analysis also reveals that network sampling intensity is a particularly relevant factor influencing network degree distribution, and that food webs appear in general more sensitive to environmental factors than other interaction types. By analysing common descriptors from the degree distributions of ecological networks, this study underscores for the first time generalities and differences across different interaction types and their response to environmental, sampling, and anthropic factors.","accessed":{"date-parts":[["2024",12,10]]},"author":[{"family":"Garcia-Callejas","given":"David"},{"family":"Thebault","given":"Elisa"},{"family":"Lajaaiti","given":"Ismael"},{"family":"Martins","given":"Lucas P."},{"family":"Laux","given":"Louise"},{"family":"Kefi","given":"Sonia"}],"citation-key":"garcia-callejasEcologicalNetworksInteraction2024","DOI":"10.1101/2024.12.04.626839","issued":{"date-parts":[["2024",12,7]]},"language":"en","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","publisher":"Ecology","source":"DOI.org (Crossref)","title":"Ecological networks across interaction types are modular and highly driven by sampling intensity at biogeographical scales","type":"article","URL":"http://biorxiv.org/lookup/doi/10.1101/2024.12.04.626839"},{"id":"gibsonSamplingMethodInfluences2011","abstract":"The search for general properties in the structure of ecological networks is currently a very active area of research. Meta-analyses of published networks are a widely used technique. To have the best chance of discovering common properties though, networks should be constructed using a standardized approach. However, this is rarely the case, and pollination networks are constructed using two main methods: transects and timed observations. To investigate the potential for variation in network structure arising from different construction techniques we constructed plant–pollinator networks using two different methods at a single site, repeating our protocol over three field seasons. Transects and timed observation methods differ in the evenness of observation effort allocated among plant species in the observed community. We show that the uneven allocation of observation effort significantly affects the number of unique interactions in the network, and we reveal a strong trend in effects on web asymmetry and evenness of marginal abundance distributions. However, these effects do not appear to extend to the higher-order properties of connectance and nestedness.","accessed":{"date-parts":[["2025",3,24]]},"author":[{"family":"Gibson","given":"Rachel H."},{"family":"Knott","given":"Ben"},{"family":"Eberlein","given":"Tim"},{"family":"Memmott","given":"Jane"}],"citation-key":"gibsonSamplingMethodInfluences2011","container-title":"Oikos","DOI":"10.1111/j.1600-0706.2010.18927.x","ISSN":"1600-0706","issue":"6","issued":{"date-parts":[["2011"]]},"language":"en","license":"© 2011 The Authors","page":"822–831","source":"Wiley Online Library","title":"Sampling method influences the structure of plant–pollinator networks","type":"article-journal","URL":"https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1600-0706.2010.18927.x","volume":"120"},{"id":"gilmerNeuralMessagePassing2017","abstract":"Supervised learning on molecules has incredible potential to be useful in chemistry, drug discovery, and materials science. Luckily, several promising and closely related neural network models invariant to molecular symmetries have already been described in the literature. These models learn a message passing algorithm and aggregation procedure to compute a function of their entire input graph. At this point, the next step is to find a particularly effective variant of this general approach and apply it to chemical prediction benchmarks until we either solve them or reach the limits of the approach. In this paper, we reformulate existing models into a single common framework we call Message Passing Neural Networks (MPNNs) and explore additional novel variations within this framework. Using MPNNs we demonstrate state of the art results on an important molecular property prediction benchmark; these results are strong enough that we believe future work should focus on datasets with larger molecules or more accurate ground truth labels.","accessed":{"date-parts":[["2024",5,15]]},"author":[{"family":"Gilmer","given":"Justin"},{"family":"Schoenholz","given":"Samuel S."},{"family":"Riley","given":"Patrick F."},{"family":"Vinyals","given":"Oriol"},{"family":"Dahl","given":"George E."}],"citation-key":"gilmerNeuralMessagePassing2017","container-title":"Proceedings of the 34th International Conference on Machine Learning","event-title":"International Conference on Machine Learning","ISSN":"2640-3498","issued":{"date-parts":[["2017",7,17]]},"language":"en","page":"1263–1272","publisher":"PMLR","source":"proceedings.mlr.press","title":"Neural Message Passing for Quantum Chemistry","type":"paper-conference","URL":"https://proceedings.mlr.press/v70/gilmer17a.html"},{"id":"glasscockWhatGraphon2016","abstract":"Graphons, short for graph functions, are limiting objects for sequences of large, finite graphs with respect to the so-called cut metric. In this expository piece, we define graphons, motivate them, and discuss how they complete the space of finite graphs. We conclude by stating three theorems that connect the finite world of graphs with the continuous world of graphons.","accessed":{"date-parts":[["2024",10,28]]},"author":[{"family":"Glasscock","given":"Daniel"}],"citation-key":"glasscockWhatGraphon2016","issued":{"date-parts":[["2016",11,2]]},"language":"en","number":"arXiv:1611.00718","publisher":"arXiv","source":"arXiv.org","title":"What is a graphon?","type":"article","URL":"http://arxiv.org/abs/1611.00718"},{"id":"gomez-mestrePhylogeneticAnalysesReveal2012","abstract":"Understanding phenotypic diversity requires not only identification of selective factors that favor origins of derived states, but also factors that favor retention of primitive states. Anurans (frogs and toads) exhibit a remarkable diversity of reproductive modes that is unique among terrestrial vertebrates. Here, we analyze the evolution of these modes, using comparative methods on a phylogeny and matched life-history database of 720 species, including most families and modes. As expected, modes with terrestrial eggs and aquatic larvae often precede direct development (terrestrial egg, no tadpole stage), but surprisingly, direct development evolves directly from aquatic breeding nearly as often. Modes with primitive exotrophic larvae (feeding outside the egg) frequently give rise to direct developers, whereas those with nonfeeding larvae (endotrophic) do not. Similarly, modes with eggs and larvae placed in locations protected from aquatic predators evolve frequently but rarely give rise to direct developers. Thus, frogs frequently bypass many seemingly intermediate stages in the evolution of direct development. We also find significant associations between terrestrial reproduction and reduced clutch size, larger egg size, reduced adult size, parental care, and occurrence in wetter and warmer regions. These associations may help explain the widespread retention of aquatic eggs and larvae, and the overall diversity of anuran reproductive modes.","accessed":{"date-parts":[["2023",11,13]]},"author":[{"family":"Gomez-Mestre","given":"Ivan"},{"family":"Pyron","given":"Robert Alexander"},{"family":"Wiens","given":"John J."}],"citation-key":"gomez-mestrePhylogeneticAnalysesReveal2012","container-title":"Evolution","DOI":"10.1111/j.1558-5646.2012.01715.x","ISSN":"1558-5646","issue":"12","issued":{"date-parts":[["2012"]]},"language":"en","license":"© 2012 The Author(s). Evolution© 2012 The Society for the Study of Evolution.","page":"3687–3700","source":"Wiley Online Library","title":"Phylogenetic Analyses Reveal Unexpected Patterns in the Evolution of Reproductive Modes in Frogs","type":"article-journal","URL":"https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1558-5646.2012.01715.x","volume":"66"},{"id":"govaertBlockClusteringBernoulli2008","abstract":"The block or simultaneous clustering problem on a set of objects and a set of variables is embedded in the mixture model. Two algorithms have been developed: block EM as part of the maximum likelihood and fuzzy approaches, and block CEM as part of the classification maximum likelihood approach. A unified framework for obtaining different variants of block EM is proposed. These variants are studied and their performances evaluated in comparison with block CEM, two-way EM and two-way CEM, i.e EM and CEM applied separately to the two sets.","accessed":{"date-parts":[["2024",11,18]]},"author":[{"family":"Govaert","given":"Gérard"},{"family":"Nadif","given":"Mohamed"}],"citation-key":"govaertBlockClusteringBernoulli2008","container-title":"Computational Statistics & Data Analysis","container-title-short":"Computational Statistics & Data Analysis","DOI":"10.1016/j.csda.2007.09.007","ISSN":"0167-9473","issue":"6","issued":{"date-parts":[["2008",2,20]]},"page":"3233–3245","source":"ScienceDirect","title":"Block clustering with Bernoulli mixture models: Comparison of different approaches","title-short":"Block clustering with Bernoulli mixture models","type":"article-journal","URL":"https://www.sciencedirect.com/science/article/pii/S0167947307003441","volume":"52"},{"id":"govaertBlockClusteringBernoulli2008","abstract":"The block or simultaneous clustering problem on a set of objects and a set of variables is embedded in the mixture model. Two algorithms have been developed: block EM as part of the maximum likelihood and fuzzy approaches, and block CEM as part of the classification maximum likelihood approach. A unified framework for obtaining different variants of block EM is proposed. These variants are studied and their performances evaluated in comparison with block CEM, two-way EM and two-way CEM, i.e EM and CEM applied separately to the two sets.","accessed":{"date-parts":[["2024",11,18]]},"author":[{"family":"Govaert","given":"Gérard"},{"family":"Nadif","given":"Mohamed"}],"citation-key":"govaertBlockClusteringBernoulli2008","container-title":"Computational Statistics & Data Analysis","DOI":"10.1016/j.csda.2007.09.007","ISSN":"0167-9473","issue":"6","issued":{"date-parts":[["2008",2,20]]},"page":"3233–3245","title":"Block clustering with Bernoulli mixture models: Comparison of different approaches","title-short":"Block clustering with Bernoulli mixture models","type":"article-journal","URL":"https://www.sciencedirect.com/science/article/pii/S0167947307003441","volume":"52"},{"id":"govaertClusteringBlockMixture2003","abstract":"Basing cluster analysis on mixture models has become a classical and powerful approach. Until now, this approach, which allows to explain some classic clustering criteria such as the well-known k-means criteria and to propose general criteria, has been developed to classify a set of objects measured on a set of variables. But, for this kind of data, if most clustering procedures are designated to construct an optimal partition of objects or, sometimes, of variables, there exist others methods, named block clustering methods, which consider simultaneously the two sets and organize the data into homogeneous blocks. In this work, a new mixture model called block mixture model is proposed to take into account this situation. This model allows to embed simultaneous clustering of objects and variables in a mixture approach. We first consider this probabilistic model in a general context and we develop a new algorithm of simultaneous partitioning based on the CEM algorithm. Then, we focus on the case of binary data and we show that our approach allows us to extend a block clustering method, which had been proposed in this case. Simplicity, fast convergence and the possibility to process large data sets are the major advantages of the proposed approach.","accessed":{"date-parts":[["2024",11,4]]},"author":[{"family":"Govaert","given":"Gérard"},{"family":"Nadif","given":"Mohamed"}],"citation-key":"govaertClusteringBlockMixture2003","collection-title":"Biometrics","container-title":"Pattern Recognition","container-title-short":"Pattern Recognition","DOI":"10.1016/S0031-3203(02)00074-2","ISSN":"0031-3203","issue":"2","issued":{"date-parts":[["2003",2,1]]},"page":"463–473","source":"ScienceDirect","title":"Clustering with block mixture models","type":"article-journal","URL":"https://www.sciencedirect.com/science/article/pii/S0031320302000742","volume":"36"},{"id":"govaertClusteringBlockMixture2003","abstract":"Basing cluster analysis on mixture models has become a classical and powerful approach. Until now, this approach, which allows to explain some classic clustering criteria such as the well-known k-means criteria and to propose general criteria, has been developed to classify a set of objects measured on a set of variables. But, for this kind of data, if most clustering procedures are designated to construct an optimal partition of objects or, sometimes, of variables, there exist others methods, named block clustering methods, which consider simultaneously the two sets and organize the data into homogeneous blocks. In this work, a new mixture model called block mixture model is proposed to take into account this situation. This model allows to embed simultaneous clustering of objects and variables in a mixture approach. We first consider this probabilistic model in a general context and we develop a new algorithm of simultaneous partitioning based on the CEM algorithm. Then, we focus on the case of binary data and we show that our approach allows us to extend a block clustering method, which had been proposed in this case. Simplicity, fast convergence and the possibility to process large data sets are the major advantages of the proposed approach.","accessed":{"date-parts":[["2024",11,4]]},"author":[{"family":"Govaert","given":"Gérard"},{"family":"Nadif","given":"Mohamed"}],"citation-key":"govaertClusteringBlockMixture2003","collection-title":"Biometrics","container-title":"Pattern Recognition","DOI":"10.1016/S0031-3203(02)00074-2","ISSN":"0031-3203","issue":"2","issued":{"date-parts":[["2003",2,1]]},"page":"463–473","title":"Clustering with block mixture models","type":"article-journal","URL":"https://www.sciencedirect.com/science/article/pii/S0031320302000742","volume":"36"},{"id":"govaertClusteringBlockMixture2003a","accessed":{"date-parts":[["2025",12,3]]},"author":[{"family":"Govaert","given":"Gérard"},{"family":"Nadif","given":"Mohamed"}],"citation-key":"govaertClusteringBlockMixture2003a","container-title":"Pattern Recognition","container-title-short":"Pattern Recognition","DOI":"10.1016/S0031-3203(02)00074-2","ISSN":"00313203","issue":"2","issued":{"date-parts":[["2003",2]]},"language":"en","license":"https://www.elsevier.com/tdm/userlicense/1.0/","note":"Read_Status: New\nRead_Status_Date: 2025-12-03T11:55:10.421Z","page":"463–473","source":"DOI.org (Crossref)","title":"Clustering with block mixture models","type":"article-journal","URL":"https://linkinghub.elsevier.com/retrieve/pii/S0031320302000742","volume":"36"},{"id":"govaertEMAlgorithmBlock2005","abstract":"Although many clustering procedures aim to construct an optimal partition of objects or, sometimes, of variables, there are other methods, called block clustering methods, which consider simultaneously the two sets and organize the data into homogeneous blocks. Recently, we have proposed a new mixture model called block mixture model which takes into account this situation. This model allows one to embed simultaneous clustering of objects and variables in a mixture approach. We have studied this probabilistic model under the classification likelihood approach and developed a new algorithm for simultaneous partitioning based on the classification EM algorithm. In this paper, we consider the block clustering problem under the maximum likelihood approach and the goal of our contribution is to estimate the parameters of this model. Unfortunately, the application of the EM algorithm for the block mixture model cannot be made directly; difficulties arise due to the dependence structure in the model and approximations are required. Using a variational approximation, we propose a generalized EM algorithm to estimate the parameters of the block mixture model and, to illustrate our approach, we study the case of binary data by using a Bernoulli block mixture.","author":[{"family":"Govaert","given":"G."},{"family":"Nadif","given":"M."}],"citation-key":"govaertEMAlgorithmBlock2005","container-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence","DOI":"10.1109/TPAMI.2005.69","event-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence","ISSN":"1939-3539","issue":"4","issued":{"date-parts":[["2005",4]]},"page":"643–647","source":"IEEE Xplore","title":"An EM algorithm for the block mixture model","type":"article-journal","volume":"27"},{"id":"govaertLatentBlockModel2010","abstract":"Although many clustering procedures aim to construct an optimal partition of objects or, sometimes, variables, there are other methods, called block clustering methods, which simultaneously consider the two sets and organize the data into homogeneous blocks. This kind of method has practical importance in a wide variety of applications such as text and market basket data analysis. Typically, the data that arise in these applications are arranged as a two-way contingency table. Using Poisson distributions, a latent block model for these data is proposed and, setting it under the maximum likelihood approach and the classification maximum likelihood approach, various algorithms are provided. Their performances are evaluated and compared to a simple use of EM or CEM applied separately on the rows and columns of the contingency table.","accessed":{"date-parts":[["2023",6,15]]},"author":[{"family":"Govaert","given":"Gérard"},{"family":"Nadif","given":"Mohamed"}],"citation-key":"govaertLatentBlockModel2010","container-title":"Communications in Statistics - Theory and Methods","DOI":"10.1080/03610920903140197","ISSN":"0361-0926","issue":"3","issued":{"date-parts":[["2010",1,13]]},"page":"416–425","publisher":"Taylor & Francis","title":"Latent Block Model for Contingency Table","type":"article-journal","URL":"https://doi.org/10.1080/03610920903140197","volume":"39"},{"id":"govaertLatentBlockModel2010a","abstract":"Although many clustering procedures aim to construct an optimal partition of objects or, sometimes, variables, there are other methods, called block clustering methods, which simultaneously consider the two sets and organize the data into homogeneous blocks. This kind of method has practical importance in a wide variety of applications such as text and market basket data analysis. Typically, the data that arise in these applications are arranged as a two-way contingency table. Using Poisson distributions, a latent block model for these data is proposed and, setting it under the maximum likelihood approach and the classification maximum likelihood approach, various algorithms are provided. Their performances are evaluated and compared to a simple use of EM or CEM applied separately on the rows and columns of the contingency table.","accessed":{"date-parts":[["2023",6,15]]},"author":[{"family":"Govaert","given":"Gérard"},{"family":"Nadif","given":"Mohamed"}],"citation-key":"govaertLatentBlockModel2010a","container-title":"Communications in Statistics - Theory and Methods","DOI":"10.1080/03610920903140197","ISSN":"0361-0926","issue":"3","issued":{"date-parts":[["2010",1,13]]},"page":"416–425","publisher":"Taylor & Francis","source":"Taylor and Francis+NEJM","title":"Latent Block Model for Contingency Table","type":"article-journal","URL":"https://doi.org/10.1080/03610920903140197","volume":"39"},{"id":"gravelExistenceAbundanceGhost2015","abstract":"In a randomly-mating biparental population of size N there are, with high probability, individuals who are genealogical ancestors of every extant individual within approximately log2(N) generations into the past. We use this result of J. Chang to prove a curious corollary under standard models of recombination: there exist, with high probability, individuals within a constant multiple of log2(N) generations into the past who are simultaneously (i) genealogical ancestors of each of the individuals at the present, and (ii) genetic ancestors to none of the individuals at the present. Such ancestral individuals–ancestors of everyone today that left no genetic trace–represent ‘ghost’ ancestors in a strong sense. In this short note, we use simple analytical argument and simulations to estimate how many such individuals exist in finite Wright–Fisher populations.","accessed":{"date-parts":[["2025",6,19]]},"author":[{"family":"Gravel","given":"Simon"},{"family":"Steel","given":"Mike"}],"citation-key":"gravelExistenceAbundanceGhost2015","container-title":"Theoretical Population Biology","container-title-short":"Theoretical Population Biology","DOI":"10.1016/j.tpb.2015.02.002","ISSN":"0040-5809","issued":{"date-parts":[["2015",5,1]]},"note":"Read_Status: New\nRead_Status_Date: 2025-06-19T15:00:59.777Z","page":"47–53","source":"ScienceDirect","title":"The existence and abundance of ghost ancestors in biparental populations","type":"article-journal","URL":"https://www.sciencedirect.com/science/article/pii/S0040580915000167","volume":"101"},{"id":"GrossSBMColSBM2025","abstract":"R package for the joint stochastic blockmodeling of collection of networks","accessed":{"date-parts":[["2025",9,25]]},"citation-key":"GrossSBMColSBM2025","genre":"R","issued":{"date-parts":[["2025",7,16]]},"note":"Read_Status: New\nRead_Status_Date: 2025-09-25T11:59:36.376Z","original-date":{"date-parts":[["2021",12,30]]},"publisher":"GroßBM","source":"GitHub","title":"GrossSBM/colSBM","type":"software","URL":"https://github.com/GrossSBM/colSBM"},{"id":"gusevaDiversityComplexityMicrobial2022","abstract":"Network analysis has been used for many years in ecological research to analyze organismal associations, for example in food webs, plant-plant or plant-animal interactions. Although network analysis is widely applied in microbial ecology, only recently has it entered the realms of soil microbial ecology, shown by a rapid rise in studies applying co-occurrence analysis to soil microbial communities. While this application offers great potential for deeper insights into the ecological structure of soil microbial ecosystems, it also brings new challenges related to the specific characteristics of soil datasets and the type of ecological questions that can be addressed. In this Perspectives Paper we assess the challenges of applying network analysis to soil microbial ecology due to the small-scale heterogeneity of the soil environment and the nature of soil microbial datasets. We review the different approaches of network construction that are commonly applied to soil microbial datasets and discuss their features and limitations. Using a test dataset of microbial communities from two depths of a forest soil, we demonstrate how different experimental designs and network constructing algorithms affect the structure of the resulting networks, and how this in turn may influence ecological conclusions. We will also reveal how assumptions of the construction method, methods of preparing the dataset, and definitions of thresholds affect the network structure. Finally, we discuss the particular questions in soil microbial ecology that can be approached by analyzing and interpreting specific network properties. Targeting these network properties in a meaningful way will allow applying this technique not in merely descriptive, but in hypothesis-driven research. Analysing microbial networks in soils opens a window to a better understanding of the complexity of microbial communities. However, this approach is unfortunately often used to draw conclusions which are far beyond the scientific evidence it can provide, which has damaged its reputation for soil microbial analysis. In this Perspectives Paper, we would like to sharpen the view for the real potential of microbial co-occurrence analysis in soils, and at the same time raise awareness regarding its limitations and the many ways how it can be misused or misinterpreted.","accessed":{"date-parts":[["2025",5,6]]},"author":[{"family":"Guseva","given":"Ksenia"},{"family":"Darcy","given":"Sean"},{"family":"Simon","given":"Eva"},{"family":"Alteio","given":"Lauren V."},{"family":"Montesinos-Navarro","given":"Alicia"},{"family":"Kaiser","given":"Christina"}],"citation-key":"gusevaDiversityComplexityMicrobial2022","container-title":"Soil Biology and Biochemistry","container-title-short":"Soil Biology and Biochemistry","DOI":"10.1016/j.soilbio.2022.108604","ISSN":"0038-0717","issued":{"date-parts":[["2022",6,1]]},"note":"Read_Status: New\nRead_Status_Date: 2025-05-07T07:43:04.957Z","page":"108604","source":"ScienceDirect","title":"From diversity to complexity: Microbial networks in soils","title-short":"From diversity to complexity","type":"article-journal","URL":"https://www.sciencedirect.com/science/article/pii/S003807172200061X","volume":"169"},{"id":"hamiltonInductiveRepresentationLearning","abstract":"Low-dimensional embeddings of nodes in large graphs have proved extremely useful in a variety of prediction tasks, from content recommendation to identifying protein functions. However, most existing approaches require that all nodes in the graph are present during training of the embeddings; these previous approaches are inherently transductive and do not naturally generalize to unseen nodes. Here we present GraphSAGE, a general inductive framework that leverages node feature information (e.g., text attributes) to efficiently generate node embeddings for previously unseen data. Instead of training individual embeddings for each node, we learn a function that generates embeddings by sampling and aggregating features from a node’s local neighborhood. Our algorithm outperforms strong baselines on three inductive node-classification benchmarks: we classify the category of unseen nodes in evolving information graphs based on citation and Reddit post data, and we show that our algorithm generalizes to completely unseen graphs using a multi-graph dataset of protein-protein interactions.","author":[{"family":"Hamilton","given":"Will"},{"family":"Ying","given":"Zhitao"},{"family":"Leskovec","given":"Jure"}],"citation-key":"hamiltonInductiveRepresentationLearning","language":"en","source":"Zotero","title":"Inductive Representation Learning on Large Graphs","type":"article-journal"},{"id":"hamiltonInductiveRepresentationLearning2018","abstract":"Low-dimensional embeddings of nodes in large graphs have proved extremely useful in a variety of prediction tasks, from content recommendation to identifying protein functions. However, most existing approaches require that all nodes in the graph are present during training of the embeddings; these previous approaches are inherently transductive and do not naturally generalize to unseen nodes. Here we present GraphSAGE, a general, inductive framework that leverages node feature information (e.g., text attributes) to efficiently generate node embeddings for previously unseen data. Instead of training individual embeddings for each node, we learn a function that generates embeddings by sampling and aggregating features from a node's local neighborhood. Our algorithm outperforms strong baselines on three inductive node-classification benchmarks: we classify the category of unseen nodes in evolving information graphs based on citation and Reddit post data, and we show that our algorithm generalizes to completely unseen graphs using a multi-graph dataset of protein-protein interactions.","accessed":{"date-parts":[["2025",7,1]]},"author":[{"family":"Hamilton","given":"William L."},{"family":"Ying","given":"Rex"},{"family":"Leskovec","given":"Jure"}],"citation-key":"hamiltonInductiveRepresentationLearning2018","DOI":"10.48550/arXiv.1706.02216","issued":{"date-parts":[["2018",9,10]]},"note":"Read_Status: New\nRead_Status_Date: 2025-07-01T13:24:50.464Z","number":"arXiv:1706.02216","publisher":"arXiv","source":"arXiv.org","title":"Inductive Representation Learning on Large Graphs","type":"article","URL":"http://arxiv.org/abs/1706.02216"},{"id":"hamiltonInductiveRepresentationLearning2018","abstract":"Low-dimensional embeddings of nodes in large graphs have proved extremely useful in a variety of prediction tasks, from content recommendation to identifying protein functions. However, most existing approaches require that all nodes in the graph are present during training of the embeddings; these previous approaches are inherently transductive and do not naturally generalize to unseen nodes. Here we present GraphSAGE, a general, inductive framework that leverages node feature information (e.g., text attributes) to efficiently generate node embeddings for previously unseen data. Instead of training individual embeddings for each node, we learn a function that generates embeddings by sampling and aggregating features from a node's local neighborhood. Our algorithm outperforms strong baselines on three inductive node-classification benchmarks: we classify the category of unseen nodes in evolving information graphs based on citation and Reddit post data, and we show that our algorithm generalizes to completely unseen graphs using a multi-graph dataset of protein-protein interactions.","accessed":{"date-parts":[["2025",7,1]]},"author":[{"family":"Hamilton","given":"William L."},{"family":"Ying","given":"Rex"},{"family":"Leskovec","given":"Jure"}],"citation-key":"hamiltonInductiveRepresentationLearning2018","DOI":"10.48550/arXiv.1706.02216","issued":{"date-parts":[["2018",9,10]]},"title":"Inductive Representation Learning on Large Graphs","type":"webpage","URL":"http://arxiv.org/abs/1706.02216"},{"id":"harreAggregateComplexityDecisions2011","accessed":{"date-parts":[["2024",2,5]]},"author":[{"family":"Harré","given":"M. S."},{"family":"Bossomaier","given":"T."},{"family":"Gillett","given":"A."},{"family":"Snyder","given":"A."}],"citation-key":"harreAggregateComplexityDecisions2011","container-title":"The European Physical Journal B","container-title-short":"Eur. Phys. J. B","DOI":"10.1140/epjb/e2011-10905-8","ISSN":"1434-6028, 1434-6036","issue":"4","issued":{"date-parts":[["2011",4]]},"language":"en","page":"555–563","source":"DOI.org (Crossref)","title":"The aggregate complexity of decisions in the game of Go","type":"article-journal","URL":"http://link.springer.com/10.1140/epjb/e2011-10905-8","volume":"80"},{"id":"harvilleMaximumLikelihoodApproaches1977","abstract":"Recent developments promise to increase greatly the popularity of maximum likelihood (ml) as a technique for estimating variance components. Patterson and Thompson (1971) proposed a restricted maximum likelihood (reml) approach which takes into account the loss in degrees of freedom resulting from estimating fixed effects. Miller (1973) developed a satisfactory asymptotic theory for ml estimators of variance components. There are many iterative algorithms that can be considered for computing the ml or reml estimates. The computations on each iteration of these algorithms are those associated with computing estimates of fixed and random effects for given values of the variance components.","accessed":{"date-parts":[["2024",3,17]]},"author":[{"family":"Harville","given":"David A."}],"citation-key":"harvilleMaximumLikelihoodApproaches1977","container-title":"Journal of the American Statistical Association","DOI":"10.1080/01621459.1977.10480998","ISSN":"0162-1459","issue":"358","issued":{"date-parts":[["1977",6,1]]},"page":"320–338","publisher":"Taylor & Francis","source":"Taylor and Francis+NEJM","title":"Maximum Likelihood Approaches to Variance Component Estimation and to Related Problems","type":"article-journal","URL":"https://www.tandfonline.com/doi/abs/10.1080/01621459.1977.10480998","volume":"72"},{"id":"hendersonDerivingInverseSum1981","abstract":"Available expressions are reviewed and new ones derived for the inverse of the sum of two matrices, one of them being nonsingular. Particular attention is given to (A + UBV)-1, where A is nonsingular and U, B and V may be rectangular; generalized inverses of A + UBV are also considered. Several statistical applications are discussed.","accessed":{"date-parts":[["2025",10,22]]},"author":[{"family":"Henderson","given":"H. V."},{"family":"Searle","given":"S. R."}],"citation-key":"hendersonDerivingInverseSum1981","container-title":"SIAM Review","ISSN":"0036-1445","issue":"1","issued":{"date-parts":[["1981"]]},"note":"Read_Status: New\nRead_Status_Date: 2025-10-22T12:32:44.903Z","page":"53–60","publisher":"Society for Industrial and Applied Mathematics","source":"JSTOR","title":"On Deriving the Inverse of a Sum of Matrices","type":"article-journal","URL":"https://www.jstor.org/stable/2029838","volume":"23"},{"id":"hendersonDerivingInverseSum1981a","abstract":"Available expressions are reviewed and new ones derived for the inverse of the sum of two matrices, one of them being nonsingular. Particular attention is given to (A + UBV)-1, where A is nonsingular and U, B and V may be rectangular; generalized inverses of A + UBV are also considered. Several statistical applications are discussed.","accessed":{"date-parts":[["2025",10,22]]},"author":[{"family":"Henderson","given":"H. V."},{"family":"Searle","given":"S. R."}],"citation-key":"hendersonDerivingInverseSum1981a","container-title":"SIAM Review","ISSN":"0036-1445","issue":"1","issued":{"date-parts":[["1981"]]},"note":"Read_Status: New\nRead_Status_Date: 2025-10-22T12:33:22.273Z","page":"53–60","publisher":"Society for Industrial and Applied Mathematics","source":"JSTOR","title":"On Deriving the Inverse of a Sum of Matrices","type":"article-journal","URL":"https://www.jstor.org/stable/2029838","volume":"23"},{"id":"heNetworkMappingRoot2021","abstract":"Understanding how plants interact with their colonizing microbiota to determine plant phenotypes is a fundamental question in modern plant science. Existing approaches for genome-wide association studies (GWAS) are often focused on the association analysis between host genes and the abundance of individual microbes, failing to characterize the genetic bases of microbial interactions that are thought to be important for microbiota structure, organization, and function. Here, we implement a behavioral model to quantify various patterns of microbe-microbe interactions, i.e., mutualism, antagonism, aggression, and altruism, and map host genes that modulate microbial networks constituted by these interaction types. We reanalyze a root-microbiome data involving 179 accessions of Arabidopsis thaliana and find that the four networks differ structurally in the pattern of bacterial-fungal interactions and microbiome complexity. We identify several fungus and bacterial hubs that play a central role in mediating microbial community assembly surrounding A. thaliana root systems. We detect 1142 significant host genetic variants throughout the plant genome and then implement Bayesian networks (BN) to reconstruct epistatic networks involving all significant SNPs, of which 91 are identified as hub QTLs. Results from gene annotation analysis suggest that most of the hub QTLs detected are in proximity to candidate genes, executing a variety of biological functions in plant growth and development, resilience against pathogens, root development, and abiotic stress resistance. This study provides a new gateway to understand how genetic variation in host plants influences microbial communities and our results could help improve crops by harnessing soil microbes.","accessed":{"date-parts":[["2025",5,6]]},"author":[{"family":"He","given":"Xiaoqing"},{"family":"Zhang","given":"Qi"},{"family":"Li","given":"Beibei"},{"family":"Jin","given":"Yi"},{"family":"Jiang","given":"Libo"},{"family":"Wu","given":"Rongling"}],"citation-key":"heNetworkMappingRoot2021","container-title":"NPJ Biofilms and Microbiomes","container-title-short":"NPJ Biofilms Microbiomes","DOI":"10.1038/s41522-021-00241-4","ISSN":"2055-5008","issued":{"date-parts":[["2021",9,7]]},"note":"Read_Status: New\nRead_Status_Date: 2025-05-07T07:43:04.957Z","page":"72","PMCID":"PMC8423736","PMID":"34493731","source":"PubMed Central","title":"Network mapping of root–microbe interactions in Arabidopsis thaliana","type":"article-journal","URL":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8423736/","volume":"7"},{"id":"hernandezClassificationGraphMetrics","abstract":"This article aims to order and classify a wide number of metrics, proposed to characterize graphs, and the services using those graphs. The number of proposed metrics over the graph history is overwhelming. Over the years, scientists constantly introduce new metrics in order to measure specific features of specific graphs. Aiming for generality, this research will focus on the classification of unweighted, undirected, general graph metrics.","author":[{"family":"Hernandez","given":"Javier Martın"},{"family":"Mieghem","given":"Piet Van"}],"citation-key":"hernandezClassificationGraphMetrics","language":"en","note":"Read_Status: New\nRead_Status_Date: 2025-11-06T14:21:49.339Z","source":"Zotero","title":"Classification of graph metrics","type":"article-journal"},{"id":"hoffAdditiveMultiplicativeEffects2021","accessed":{"date-parts":[["2026",1,23]]},"author":[{"family":"Hoff","given":"Peter"}],"citation-key":"hoffAdditiveMultiplicativeEffects2021","container-title":"Statistical Science","container-title-short":"Statist. Sci.","DOI":"10.1214/19-STS757","ISSN":"0883-4237","issue":"1","issued":{"date-parts":[["2021",2,1]]},"note":"Read_Status: New\nRead_Status_Date: 2026-01-23T12:38:27.731Z","source":"DOI.org (Crossref)","title":"Additive and Multiplicative Effects Network Models","type":"article-journal","URL":"https://projecteuclid.org/journals/statistical-science/volume-36/issue-1/Additive-and-Multiplicative-Effects-Network-Models/10.1214/19-STS757.full","volume":"36"},{"id":"hoffLatentSpaceApproaches2002","abstract":"Network models are widely used to represent relational information among interacting units. In studies of social networks, recent emphasis has been placed on random graph models where the nodes usually represent individual social actors and the edges represent the presence of a specified relation between actors. We develop a class of models where the probability of a relation between actors depends on the positions of individuals in an unobserved “social space.” We make inference for the social space within maximum likelihood and Bayesian frameworks, and propose Markov chain Monte Carlo procedures for making inference on latent positions and the effects of observed covariates. We present analyses of three standard datasets from the social networks literature, and compare the method to an alternative stochastic blockmodeling approach. In addition to improving on model fit for these datasets, our method provides a visual and interpretable model-based spatial representation of social relationships and improves on existing methods by allowing the statistical uncertainty in the social space to be quantified and graphically represented.","accessed":{"date-parts":[["2024",5,20]]},"author":[{"family":"Hoff","given":"Peter D"},{"family":"Raftery","given":"Adrian E"},{"family":"Handcock","given":"Mark S"}],"citation-key":"hoffLatentSpaceApproaches2002","container-title":"Journal of the American Statistical Association","DOI":"10.1198/016214502388618906","ISSN":"0162-1459","issue":"460","issued":{"date-parts":[["2002",12,1]]},"page":"1090–1098","publisher":"Taylor & Francis","source":"Taylor and Francis+NEJM","title":"Latent Space Approaches to Social Network Analysis","type":"article-journal","URL":"https://doi.org/10.1198/016214502388618906","volume":"97"},{"id":"hoffLatentSpaceApproaches2002a","abstract":"Network models are widely used to represent relational information among interacting units. In studies of social networks, recent emphasis has been placed on random graph models where the nodes usually represent individual social actors and the edges represent the presence of a specified relation between actors. We develop a class of models where the probability of a relation between actors depends on the positions of individuals in an unobserved “social space.” We make inference for the social space within maximum likelihood and Bayesian frameworks, and propose Markov chain Monte Carlo procedures for making inference on latent positions and the effects of observed covariates. We present analyses of three standard datasets from the social networks literature, and compare the method to an alternative stochastic blockmodeling approach. In addition to improving on model fit for these datasets, our method provides a visual and interpretable model-based spatial representation of social relationships and improves on existing methods by allowing the statistical uncertainty in the social space to be quantified and graphically represented.","accessed":{"date-parts":[["2024",11,15]]},"author":[{"family":"Hoff","given":"Peter D"},{"family":"Raftery","given":"Adrian E"},{"family":"Handcock","given":"Mark S"}],"citation-key":"hoffLatentSpaceApproaches2002a","container-title":"Journal of the American Statistical Association","DOI":"10.1198/016214502388618906","ISSN":"0162-1459","issue":"460","issued":{"date-parts":[["2002",12,1]]},"page":"1090–1098","publisher":"ASA Website","source":"Taylor and Francis+NEJM","title":"Latent Space Approaches to Social Network Analysis","type":"article-journal","URL":"https://doi.org/10.1198/016214502388618906","volume":"97"},{"id":"hollandStochasticBlockmodelsFirst1983","abstract":"A stochastic model is proposed for social networks in which the actors in a network are partitioned into subgroups called blocks. The model provides a stochastic generalization of the blockmodel. Estimation techniques are developed for the special case of a single relation social network, with blocks specified a priori. An extension of the model allows for tendencies toward reciprocation of ties beyond those explained by the partition. The extended model provides a one degree-of-freedom test of the model. A numerical example from the social network literature is used to illustrate the methods.","accessed":{"date-parts":[["2023",6,15]]},"author":[{"family":"Holland","given":"Paul W."},{"family":"Laskey","given":"Kathryn Blackmond"},{"family":"Leinhardt","given":"Samuel"}],"citation-key":"hollandStochasticBlockmodelsFirst1983","container-title":"Social Networks","container-title-short":"Social Networks","DOI":"10.1016/0378-8733(83)90021-7","ISSN":"0378-8733","issue":"2","issued":{"date-parts":[["1983",6,1]]},"language":"en","page":"109–137","source":"ScienceDirect","title":"Stochastic blockmodels: First steps","title-short":"Stochastic blockmodels","type":"article-journal","URL":"https://www.sciencedirect.com/science/article/pii/0378873383900217","volume":"5"},{"id":"hronImputationMissingValues2010","abstract":"New imputation algorithms for estimating missing values in compositional data are introduced. A first proposal uses the k-nearest neighbor procedure based on the Aitchison distance, a distance measure especially designed for compositional data. It is important to adjust the estimated missing values to the overall size of the compositional parts of the neighbors. As a second proposal an iterative model-based imputation technique is introduced which initially starts from the result of the proposed k-nearest neighbor procedure. The method is based on iterative regressions, thereby accounting for the whole multivariate data information. The regressions have to be performed in a transformed space, and depending on the data quality classical or robust regression techniques can be employed. The proposed methods are tested on a real and on simulated data sets. The results show that the proposed methods outperform standard imputation methods. In the presence of outliers, the model-based method with robust regressions is preferable.","accessed":{"date-parts":[["2026",4,17]]},"author":[{"family":"Hron","given":"K."},{"family":"Templ","given":"M."},{"family":"Filzmoser","given":"P."}],"citation-key":"hronImputationMissingValues2010","container-title":"Computational Statistics & Data Analysis","container-title-short":"Computational Statistics & Data Analysis","DOI":"10.1016/j.csda.2009.11.023","ISSN":"01679473","issue":"12","issued":{"date-parts":[["2010",12]]},"language":"en","license":"https://www.elsevier.com/tdm/userlicense/1.0/","note":"Read_Status: New\nRead_Status_Date: 2026-04-17T16:14:09.172Z","page":"3095–3107","source":"DOI.org (Crossref)","title":"Imputation of missing values for compositional data using classical and robust methods","type":"article-journal","URL":"https://linkinghub.elsevier.com/retrieve/pii/S0167947309004368","volume":"54"},{"id":"HttpsApiistexfrArk","accessed":{"date-parts":[["2025",4,11]]},"citation-key":"HttpsApiistexfrArk","title":"https://api.istex.fr/ark:/67375/VQC-RFRMVDW8-W/fulltext.pdf?sid=clickandread","type":"webpage","URL":"https://api.istex.fr/ark:/67375/VQC-RFRMVDW8-W/fulltext.pdf?sid=clickandread"},{"id":"HttpsPublimathunidebhuLoad_doiphppdoi10_5486_PMD_1959_6_3_4_12","accessed":{"date-parts":[["2024",8,9]]},"citation-key":"HttpsPublimathunidebhuLoad_doiphppdoi10_5486_PMD_1959_6_3_4_12","title":"https://publi.math.unideb.hu/load_doi.php?pdoi=10_5486_PMD_1959_6_3_4_12","type":"webpage","URL":"https://publi.math.unideb.hu/load_doi.php?pdoi=10_5486_PMD_1959_6_3_4_12"},{"id":"HttpsWwwavaresearchcomFiles","accessed":{"date-parts":[["2024",3,11]]},"citation-key":"HttpsWwwavaresearchcomFiles","title":"https://www.avaresearch.com/files/UnskilledAndUnawareOfIt.pdf","type":"webpage","URL":"https://www.avaresearch.com/files/UnskilledAndUnawareOfIt.pdf"},{"id":"hubertComparingPartitions1985","abstract":"The problem of comparing two different partitions of a finite set of objects reappears continually in the clustering literature. We begin by reviewing a well-known measure of partition correspondence often attributed to Rand (1971), discuss the issue of correcting this index for chance, and note that a recent normalization strategy developed by Morey and Agresti (1984) and adopted by others (e.g., Miligan and Cooper 1985) is based on an incorrect assumption. Then, the general problem of comparing partitions is approached indirectly by assessing the congruence of two proximity matrices using a simple cross-product measure. They are generated from corresponding partitions using various scoring rules. Special cases derivable include traditionally familiar statistics and/or ones tailored to weight certain object pairs differentially. Finally, we propose a measure based on the comparison of object triples having the advantage of a probabilistic interpretation in addition to being corrected for chance (i.e., assuming a constant value under a reasonable null hypothesis) and bounded between ±1.","accessed":{"date-parts":[["2023",7,4]]},"author":[{"family":"Hubert","given":"Lawrence"},{"family":"Arabie","given":"Phipps"}],"citation-key":"hubertComparingPartitions1985","container-title":"Journal of Classification","container-title-short":"Journal of Classification","DOI":"10.1007/BF01908075","ISSN":"1432-1343","issue":"1","issued":{"date-parts":[["1985",12,1]]},"language":"en","page":"193–218","source":"Springer Link","title":"Comparing partitions","type":"article-journal","URL":"https://doi.org/10.1007/BF01908075","volume":"2"},{"id":"hubertComparingPartitions1985","abstract":"The problem of comparing two different partitions of a finite set of objects reappears continually in the clustering literature. We begin by reviewing a well-known measure of partition correspondence often attributed to Rand (1971), discuss the issue of correcting this index for chance, and note that a recent normalization strategy developed by Morey and Agresti (1984) and adopted by others (e.g., Miligan and Cooper 1985) is based on an incorrect assumption. Then, the general problem of comparing partitions is approached indirectly by assessing the congruence of two proximity matrices using a simple cross-product measure. They are generated from corresponding partitions using various scoring rules. Special cases derivable include traditionally familiar statistics and/or ones tailored to weight certain object pairs differentially. Finally, we propose a measure based on the comparison of object triples having the advantage of a probabilistic interpretation in addition to being corrected for chance (i.e., assuming a constant value under a reasonable null hypothesis) and bounded between ±1.","accessed":{"date-parts":[["2023",7,4]]},"author":[{"family":"Hubert","given":"Lawrence"},{"family":"Arabie","given":"Phipps"}],"citation-key":"hubertComparingPartitions1985","container-title":"Journal of Classification","DOI":"10.1007/BF01908075","ISSN":"1432-1343","issue":"1","issued":{"date-parts":[["1985",12,1]]},"language":"english","page":"193–218","title":"Comparing partitions","type":"article-journal","URL":"https://doi.org/10.1007/BF01908075","volume":"2"},{"id":"Introduction1990","abstract":"The prelims comprise: Motivation Types of Data and How to Handle Them Which Clustering Algorithm to Choose A Schematic Overview of Our Programs Computing Dissimilarities with the Program DAISY","accessed":{"date-parts":[["2024",9,13]]},"citation-key":"Introduction1990","container-title":"Finding Groups in Data","DOI":"10.1002/9780470316801.ch1","ISBN":"978-0-470-31680-1","issued":{"date-parts":[["1990"]]},"language":"en","page":"1–67","publisher":"John Wiley & Sons, Ltd","section":"1","source":"Wiley Online Library","title":"Introduction","type":"chapter","URL":"https://onlinelibrary.wiley.com/doi/abs/10.1002/9780470316801.ch1"},{"id":"IntroductionDonjonsDragons","accessed":{"date-parts":[["2024",4,13]]},"citation-key":"IntroductionDonjonsDragons","title":"Introduction » Donjons & Dragons - D&D 5e","type":"webpage","URL":"https://www.aidedd.org/regles/"},{"id":"jianRestrictedTweedieStochastic","abstract":"The stochastic block model (SBM) is a widely used framework for community detection in networks, where the network structure is typically represented by an adjacency matrix. However, conventional SBMs are not directly applicable to an adjacency matrix that consists of nonnegative zero-inflated continuous edge weights. To model the international trading network, where edge weights represent trading values between countries, we propose an SBM based on a restricted Tweedie distribution. Additionally, we incorporate nodal information, such as the geographical distance between countries, and account for its dynamic effect on edge weights. Notably, we show that given a sufficiently large number of nodes, estimating this covariate effect becomes independent of community labels of each node when computing the maximum likelihood estimator of parameters in our model. This result enables the development of an efficient two-step algorithm that separates the estimation of covariate effects from other parameters. We demonstrate the effectiveness of our proposed method through extensive simulation studies and an application to international trading data.","accessed":{"date-parts":[["2026",1,7]]},"author":[{"family":"Jian","given":"Jie"},{"family":"Zhu","given":"Mu"},{"family":"Sang","given":"Peijun"}],"citation-key":"jianRestrictedTweedieStochastic","container-title":"Canadian Journal of Statistics","DOI":"10.1002/cjs.70012","ISSN":"1708-945X","issue":"n/a","language":"en","note":"Read_Status: New\nRead_Status_Date: 2026-01-07T14:24:57.323Z","page":"e70012","source":"Wiley Online Library","title":"Restricted Tweedie stochastic block models","type":"article-journal","URL":"https://onlinelibrary.wiley.com/doi/abs/10.1002/cjs.70012","volume":"n/a"},{"id":"jiOverviewLargeLanguage2025","abstract":"Large Language Models (LLMs) have emerged as transformative tools in artificial intelligence (AI), exhibiting remarkable capabilities across diverse tasks such as text generation, reasoning, and decision-making. While their success has primarily been driven by advances in computational power and deep learning architectures, emerging problems -- in areas such as uncertainty quantification, decision-making, causal inference, and distribution shift -- require a deeper engagement with the field of statistics. This paper explores potential areas where statisticians can make important contributions to the development of LLMs, particularly those that aim to engender trustworthiness and transparency for human users. Thus, we focus on issues such as uncertainty quantification, interpretability, fairness, privacy, watermarking and model adaptation. We also consider possible roles for LLMs in statistical analysis. By bridging AI and statistics, we aim to foster a deeper collaboration that advances both the theoretical foundations and practical applications of LLMs, ultimately shaping their role in addressing complex societal challenges.","accessed":{"date-parts":[["2025",3,10]]},"author":[{"family":"Ji","given":"Wenlong"},{"family":"Yuan","given":"Weizhe"},{"family":"Getzen","given":"Emily"},{"family":"Cho","given":"Kyunghyun"},{"family":"Jordan","given":"Michael I."},{"family":"Mei","given":"Song"},{"family":"Weston","given":"Jason E."},{"family":"Su","given":"Weijie J."},{"family":"Xu","given":"Jing"},{"family":"Zhang","given":"Linjun"}],"citation-key":"jiOverviewLargeLanguage2025","issued":{"date-parts":[["2025",2,24]]},"number":"arXiv:2502.17814","source":"arXiv.org","title":"An Overview of Large Language Models for Statisticians","type":"article","URL":"http://arxiv.org/abs/2502.17814"},{"id":"joksasJoksasHugosimplecite2024","abstract":"Citations in Hugo websites.","accessed":{"date-parts":[["2024",4,13]]},"author":[{"family":"Joksas","given":"Dovydas"}],"citation-key":"joksasJoksasHugosimplecite2024","genre":"HTML","issued":{"date-parts":[["2024",3,14]]},"license":"MIT","original-date":{"date-parts":[["2021",5,3]]},"source":"GitHub","title":"joksas/hugo-simplecite","type":"software","URL":"https://github.com/joksas/hugo-simplecite"},{"id":"jordanoBiodiversityPlantfrugivoreInteractions","abstract":"Pairwise plant-frugivore mutualistic interactions build up into mega-diverse networks involving dozens of interacting species, being the most generalized among free-living species. These mutualisms consist of food provisioning by plants and, their counterpart, plant propagule (seeds) movement by the animals, being crucial for the natural vegetation regeneration in many ecosystems. Yet we are far from understanding which part of this enormous interaction biodiversity is needed for their maintenance. I overview the diversity of interaction modes involved in these mutualisms, the main components of the seed dispersal services, and their functional diversity. I examine how interaction richness covaries with partner species richness at different scales, resulting in variable patterns of species complementarities in terms of seed dispersal effects. The functionality of most generalized plant-frugivore mutualisms relies on complementarity of effects across a high diversity of partners, yet frequently depends on just a distinct subset of them, resulting in high functional redundancy. Two distinct aspects are relevant: 1) variable quantitative effects among species; 2) variable pairwise-interaction outcomes, between the extremes of antagonism and mutualism. Frugivory, occurring at the [inal stage of each plant reproductive episode, entails a large, cumulative, effect of other biotic interactions occurring at earlier stages (e.g., [loral herbivory, pollination, pre-dispersal fruit damage). I examine how plant-frugivore interactions mixup with the whole biotic interactome of a plant, using the Prunus mahaleb system as a case study. The effects of distinct subsets of frugivores combine with different sets of antagonistic and mutualistic partners in other interactions, yet having a lasting signal on [inal seed dispersal success.","author":[{"family":"Jordano","given":"Pedro"}],"citation-key":"jordanoBiodiversityPlantfrugivoreInteractions","language":"en","note":"Read_Status: New\nRead_Status_Date: 2025-12-01T08:49:25.634Z","source":"Zotero","title":"The biodiversity of plant-frugivore interactions: types, functions, and consequences","type":"article-journal"},{"id":"joshvartyAlphaZeroMonte2020","abstract":"Blog: http://joshvarty.github.io/AlphaZero/\nGitHub: https://github.com/JoshVarty/AlphaZer...\nTwitch: / joshvarty \n\nA discussion of Alpha Zero and Monte Carlo Tree Search","accessed":{"date-parts":[["2024",2,8]]},"citation-key":"joshvartyAlphaZeroMonte2020","dimensions":"23:34","director":[{"literal":"Josh Varty"}],"issued":{"date-parts":[["2020",5,24]]},"source":"YouTube","title":"Alpha Zero and Monte Carlo Tree Search","type":"motion_picture","URL":"https://www.youtube.com/watch?v=62nq4Zsn8vc"},{"id":"karrerStochasticBlockmodelsCommunity2011","abstract":"Stochastic blockmodels have been proposed as a tool for detecting community structure in networks as well as for generating synthetic networks for use as benchmarks. Most blockmodels, however, ignore variation in vertex degree, making them unsuitable for applications to real-world networks, which typically display broad degree distributions that can significantly distort the results. Here we demonstrate how the generalization of blockmodels to incorporate this missing element leads to an improved objective function for community detection in complex networks. We also propose a heuristic algorithm for community detection using this objective function or its non-degree-corrected counterpart and show that the degree-corrected version dramatically outperforms the uncorrected one in both real-world and synthetic networks.","accessed":{"date-parts":[["2025",9,26]]},"author":[{"family":"Karrer","given":"Brian"},{"family":"Newman","given":"M. E. J."}],"citation-key":"karrerStochasticBlockmodelsCommunity2011","container-title":"Physical Review E","container-title-short":"Phys. Rev. E","DOI":"10.1103/PhysRevE.83.016107","ISSN":"1539-3755, 1550-2376","issue":"1","issued":{"date-parts":[["2011",1,21]]},"language":"en","note":"Read_Status: New\nRead_Status_Date: 2025-09-26T08:18:22.155Z","page":"016107","source":"arXiv.org","title":"Stochastic blockmodels and community structure in networks","type":"article-journal","URL":"http://arxiv.org/abs/1008.3926","volume":"83"},{"id":"kaszewska-gilasGlobalStudiesHostParasite2021","abstract":"The quill mites belonging to the family Syringophilidae (Acari: Prostigmata: Cheyletoidea) are obligate ectoparasites of birds. They inhabit different types of the quills, where they spend their whole life cycle. In this paper, we conducted a global study of syringophilid mites associated with columbiform birds. We examined 772 pigeon and dove individuals belonging to 112 species (35% world fauna) from all zoogeographical regions (except Madagascan) where Columbiformes occur. We measured the prevalence (IP) and the confidence interval (CI) for all infested host species. IP ranges between 4.2 and 66.7 (CI 0.2–100). We applied a bipartite analysis to determine host–parasite interaction, network indices, and host specificity on species and whole network levels. The Syringophilidae–Columbiformes network was composed of 25 mite species and 65 host species. The bipartite network was characterized by a high network level specialization H2′ = 0.93, high nestedness N = 0.908, connectance C = 0.90, and high modularity Q = 0.83, with 20 modules. Moreover, we reconstructed the phylogeny of the quill mites associated with columbiform birds on the generic level. Analysis shows two distinct clades: Meitingsunes + Psittaciphilus, and Peristerophila + Terratosyringophilus.","accessed":{"date-parts":[["2023",6,15]]},"author":[{"family":"Kaszewska-Gilas","given":"Katarzyna"},{"family":"Kosicki","given":"Jakub Ziemowit"},{"family":"Hromada","given":"Martin"},{"family":"Skoracki","given":"Maciej"}],"citation-key":"kaszewska-gilasGlobalStudiesHostParasite2021","container-title":"Animals","DOI":"10.3390/ani11123392","ISSN":"2076-2615","issue":"12","issued":{"date-parts":[["2021",12]]},"language":"english","page":"3392","publisher":"Multidisciplinary Digital Publishing Institute","title":"Global Studies of the Host-Parasite Relationships between Ectoparasitic Mites of the Family Syringophilidae and Birds of the Order Columbiformes","type":"article-journal","URL":"https://www.mdpi.com/2076-2615/11/12/3392","volume":"11"},{"id":"kaszewska-gilasGlobalStudiesHostParasite2021a","abstract":"The quill mites belonging to the family Syringophilidae (Acari: Prostigmata: Cheyletoidea) are obligate ectoparasites of birds. They inhabit different types of the quills, where they spend their whole life cycle. In this paper, we conducted a global study of syringophilid mites associated with columbiform birds. We examined 772 pigeon and dove individuals belonging to 112 species (35% world fauna) from all zoogeographical regions (except Madagascan) where Columbiformes occur. We measured the prevalence (IP) and the confidence interval (CI) for all infested host species. IP ranges between 4.2 and 66.7 (CI 0.2–100). We applied a bipartite analysis to determine host–parasite interaction, network indices, and host specificity on species and whole network levels. The Syringophilidae–Columbiformes network was composed of 25 mite species and 65 host species. The bipartite network was characterized by a high network level specialization H2′ = 0.93, high nestedness N = 0.908, connectance C = 0.90, and high modularity Q = 0.83, with 20 modules. Moreover, we reconstructed the phylogeny of the quill mites associated with columbiform birds on the generic level. Analysis shows two distinct clades: Meitingsunes + Psittaciphilus, and Peristerophila + Terratosyringophilus.","accessed":{"date-parts":[["2023",6,15]]},"author":[{"family":"Kaszewska-Gilas","given":"Katarzyna"},{"family":"Kosicki","given":"Jakub Ziemowit"},{"family":"Hromada","given":"Martin"},{"family":"Skoracki","given":"Maciej"}],"citation-key":"kaszewska-gilasGlobalStudiesHostParasite2021a","container-title":"Animals","DOI":"10.3390/ani11123392","ISSN":"2076-2615","issue":"12","issued":{"date-parts":[["2021",12]]},"language":"en","license":"http://creativecommons.org/licenses/by/3.0/","number":"12","page":"3392","publisher":"Multidisciplinary Digital Publishing Institute","source":"www.mdpi.com","title":"Global Studies of the Host-Parasite Relationships between Ectoparasitic Mites of the Family Syringophilidae and Birds of the Order Columbiformes","type":"article-journal","URL":"https://www.mdpi.com/2076-2615/11/12/3392","volume":"11"},{"id":"kaufmanFindingGroupsData1990","accessed":{"date-parts":[["2024",9,13]]},"author":[{"family":"Kaufman","given":"Leonard"},{"family":"Rousseeuw","given":"Peter J."}],"citation-key":"kaufmanFindingGroupsData1990","collection-title":"Wiley Series in Probability and Statistics","DOI":"10.1002/9780470316801","edition":"1","ISBN":"978-0-471-87876-6 978-0-470-31680-1","issued":{"date-parts":[["1990",3,8]]},"language":"en","license":"http://doi.wiley.com/10.1002/tdm_license_1.1","publisher":"Wiley","source":"DOI.org (Crossref)","title":"Finding Groups in Data: An Introduction to Cluster Analysis","title-short":"Finding Groups in Data","type":"book","URL":"https://onlinelibrary.wiley.com/doi/book/10.1002/9780470316801"},{"id":"kaurLatentPositionNetwork2023","abstract":"In this chapter, we present a review of latent position models for networks. We review the recent literature in this area and illustrate the basic aspects and properties of this modeling framework. Through several illustrative examples we highlight how the latent position model is able to capture important features of observed networks. We emphasize how the canonical design of this model has made it popular thanks to its ability to provide interpretable visualizations of complex network interactions. We outline the main extensions that have been introduced to this model, illustrating its flexibility and applicability.","accessed":{"date-parts":[["2026",1,23]]},"author":[{"family":"Kaur","given":"Hardeep"},{"family":"Rastelli","given":"Riccardo"},{"family":"Friel","given":"Nial"},{"family":"Raftery","given":"Adrian E."}],"citation-key":"kaurLatentPositionNetwork2023","DOI":"10.48550/arXiv.2304.02979","issued":{"date-parts":[["2023",4,6]]},"note":"Read_Status: New\nRead_Status_Date: 2026-01-23T09:21:03.948Z","number":"arXiv:2304.02979","publisher":"arXiv","source":"arXiv.org","title":"Latent Position Network Models","type":"article","URL":"http://arxiv.org/abs/2304.02979"},{"id":"keribinEstimationSelectionLatent2015","abstract":"This paper deals with estimation and model selection in the Latent Block Model (LBM) for categorical data. First, after providing sufficient conditions ensuring the identifiability of this model, we generalise estimation procedures and model selection criteria derived for binary data. Secondly, we develop Bayesian inference through Gibbs sampling and with a well calibrated non informative prior distribution, in order to get the MAP estimator: this is proved to avoid the traps encountered by the LBM with the maximum likelihood methodology. Then model selection criteria are presented. In particular an exact expression of the integrated completed likelihood criterion requiring no asymptotic approximation is derived. Finally numerical experiments on both simulated and real data sets highlight the appeal of the proposed estimation and model selection procedures.","accessed":{"date-parts":[["2024",5,15]]},"author":[{"family":"Keribin","given":"Christine"},{"family":"Brault","given":"Vincent"},{"family":"Celeux","given":"Gilles"},{"family":"Govaert","given":"Gérard"}],"citation-key":"keribinEstimationSelectionLatent2015","container-title":"Statistics and Computing","container-title-short":"Stat Comput","DOI":"10.1007/s11222-014-9472-2","ISSN":"1573-1375","issue":"6","issued":{"date-parts":[["2015",11,1]]},"language":"en","page":"1201–1216","source":"Springer Link","title":"Estimation and selection for the latent block model on categorical data","type":"article-journal","URL":"https://doi.org/10.1007/s11222-014-9472-2","volume":"25"},{"id":"keribinEstimationSelectionLatent2015","abstract":"This paper deals with estimation and model selection in the Latent Block Model (LBM) for categorical data. First, after providing sufficient conditions ensuring the identifiability of this model, we generalise estimation procedures and model selection criteria derived for binary data. Secondly, we develop Bayesian inference through Gibbs sampling and with a well calibrated non informative prior distribution, in order to get the MAP estimator: this is proved to avoid the traps encountered by the LBM with the maximum likelihood methodology. Then model selection criteria are presented. In particular an exact expression of the integrated completed likelihood criterion requiring no asymptotic approximation is derived. Finally numerical experiments on both simulated and real data sets highlight the appeal of the proposed estimation and model selection procedures.","accessed":{"date-parts":[["2024",5,15]]},"author":[{"family":"Keribin","given":"Christine"},{"family":"Brault","given":"Vincent"},{"family":"Celeux","given":"Gilles"},{"family":"Govaert","given":"Gérard"}],"citation-key":"keribinEstimationSelectionLatent2015","container-title":"Statistics and computing","container-title-short":"Stat Comput","DOI":"10.1007/s11222-014-9472-2","ISSN":"1573-1375","issue":"6","issued":{"date-parts":[["2015",11,1]]},"language":"english","page":"1201–1216","title":"Estimation and selection for the latent block model on categorical data","type":"article-journal","URL":"https://doi.org/10.1007/s11222-014-9472-2","volume":"25"},{"id":"kernighanEfficientHeuristicProcedure1970","abstract":"We consider the problem of partitioning the nodes of a graph with costs on its edges into subsets of given sizes so as to minimize the sum of the costs on all edges cut. This problem arises in several physical situations — for example, in assigning the components of electronic circuits to circuit boards to minimize the number of connections between boards. This paper presents a heuristic method for partitioning arbitrary graphs which is both effective in finding optimal partitions, and fast enough to be practical in solving large problems.","accessed":{"date-parts":[["2025",1,26]]},"author":[{"family":"Kernighan","given":"B. W."},{"family":"Lin","given":"S."}],"citation-key":"kernighanEfficientHeuristicProcedure1970","container-title":"The Bell System Technical Journal","DOI":"10.1002/j.1538-7305.1970.tb01770.x","event-title":"The Bell System Technical Journal","ISSN":"0005-8580","issue":"2","issued":{"date-parts":[["1970",2]]},"page":"291–307","source":"IEEE Xplore","title":"An efficient heuristic procedure for partitioning graphs","type":"article-journal","URL":"https://ieeexplore.ieee.org/abstract/document/6771089","volume":"49"},{"id":"kingmaAutoEncodingVariationalBayes2022","abstract":"How can we perform efficient inference and learning in directed probabilistic models, in the presence of continuous latent variables with intractable posterior distributions, and large datasets? We introduce a stochastic variational inference and learning algorithm that scales to large datasets and, under some mild differentiability conditions, even works in the intractable case. Our contributions are two-fold. First, we show that a reparameterization of the variational lower bound yields a lower bound estimator that can be straightforwardly optimized using standard stochastic gradient methods. Second, we show that for i.i.d. datasets with continuous latent variables per datapoint, posterior inference can be made especially efficient by fitting an approximate inference model (also called a recognition model) to the intractable posterior using the proposed lower bound estimator. Theoretical advantages are reflected in experimental results.","accessed":{"date-parts":[["2024",5,21]]},"author":[{"family":"Kingma","given":"Diederik P."},{"family":"Welling","given":"Max"}],"citation-key":"kingmaAutoEncodingVariationalBayes2022","DOI":"10.48550/arXiv.1312.6114","issued":{"date-parts":[["2022",12,10]]},"number":"arXiv:1312.6114","publisher":"arXiv","source":"arXiv.org","title":"Auto-Encoding Variational Bayes","type":"article","URL":"http://arxiv.org/abs/1312.6114"},{"id":"kingmaAutoEncodingVariationalBayes2022a","abstract":"How can we perform efficient inference and learning in directed probabilistic models, in the presence of continuous latent variables with intractable posterior distributions, and large datasets? We introduce a stochastic variational inference and learning algorithm that scales to large datasets and, under some mild differentiability conditions, even works in the intractable case. Our contributions are two-fold. First, we show that a reparameterization of the variational lower bound yields a lower bound estimator that can be straightforwardly optimized using standard stochastic gradient methods. Second, we show that for i.i.d. datasets with continuous latent variables per datapoint, posterior inference can be made especially efficient by fitting an approximate inference model (also called a recognition model) to the intractable posterior using the proposed lower bound estimator. Theoretical advantages are reflected in experimental results.","accessed":{"date-parts":[["2024",2,20]]},"author":[{"family":"Kingma","given":"Diederik P."},{"family":"Welling","given":"Max"}],"citation-key":"kingmaAutoEncodingVariationalBayes2022a","DOI":"10.48550/arXiv.1312.6114","issued":{"date-parts":[["2022",12,10]]},"number":"arXiv:1312.6114","publisher":"arXiv","source":"arXiv.org","title":"Auto-Encoding Variational Bayes","type":"article","URL":"http://arxiv.org/abs/1312.6114"},{"id":"kingmaAutoEncodingVariationalBayes2022b","abstract":"How can we perform efficient inference and learning in directed probabilistic models, in the presence of continuous latent variables with intractable posterior distributions, and large datasets? We introduce a stochastic variational inference and learning algorithm that scales to large datasets and, under some mild differentiability conditions, even works in the intractable case. Our contributions are two-fold. First, we show that a reparameterization of the variational lower bound yields a lower bound estimator that can be straightforwardly optimized using standard stochastic gradient methods. Second, we show that for i.i.d. datasets with continuous latent variables per datapoint, posterior inference can be made especially efficient by fitting an approximate inference model (also called a recognition model) to the intractable posterior using the proposed lower bound estimator. Theoretical advantages are reflected in experimental results.","accessed":{"date-parts":[["2024",2,20]]},"author":[{"family":"Kingma","given":"Diederik P."},{"family":"Welling","given":"Max"}],"citation-key":"kingmaAutoEncodingVariationalBayes2022b","DOI":"10.48550/arXiv.1312.6114","issued":{"date-parts":[["2022",12,10]]},"number":"arXiv:1312.6114","publisher":"arXiv","source":"arXiv.org","title":"Auto-Encoding Variational Bayes","type":"article","URL":"http://arxiv.org/abs/1312.6114"},{"id":"kingmaAutoEncodingVariationalBayes2022c","abstract":"How can we perform efficient inference and learning in directed probabilistic models, in the presence of continuous latent variables with intractable posterior distributions, and large datasets? We introduce a stochastic variational inference and learning algorithm that scales to large datasets and, under some mild differentiability conditions, even works in the intractable case. Our contributions are two-fold. First, we show that a reparameterization of the variational lower bound yields a lower bound estimator that can be straightforwardly optimized using standard stochastic gradient methods. Second, we show that for i.i.d. datasets with continuous latent variables per datapoint, posterior inference can be made especially efficient by fitting an approximate inference model (also called a recognition model) to the intractable posterior using the proposed lower bound estimator. Theoretical advantages are reflected in experimental results.","accessed":{"date-parts":[["2024",2,19]]},"author":[{"family":"Kingma","given":"Diederik P."},{"family":"Welling","given":"Max"}],"citation-key":"kingmaAutoEncodingVariationalBayes2022c","DOI":"10.48550/arXiv.1312.6114","issued":{"date-parts":[["2022",12,10]]},"number":"arXiv:1312.6114","publisher":"arXiv","source":"arXiv.org","title":"Auto-Encoding Variational Bayes","type":"article","URL":"http://arxiv.org/abs/1312.6114"},{"id":"kipfSemiSupervisedClassificationGraph2017","abstract":"We present a scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs. We motivate the choice of our convolutional architecture via a localized first-order approximation of spectral graph convolutions. Our model scales linearly in the number of graph edges and learns hidden layer representations that encode both local graph structure and features of nodes. In a number of experiments on citation networks and on a knowledge graph dataset we demonstrate that our approach outperforms related methods by a significant margin.","accessed":{"date-parts":[["2024",5,14]]},"author":[{"family":"Kipf","given":"Thomas N."},{"family":"Welling","given":"Max"}],"citation-key":"kipfSemiSupervisedClassificationGraph2017","DOI":"10.48550/arXiv.1609.02907","issued":{"date-parts":[["2017",2,22]]},"number":"arXiv:1609.02907","publisher":"arXiv","source":"arXiv.org","title":"Semi-Supervised Classification with Graph Convolutional Networks","type":"article","URL":"http://arxiv.org/abs/1609.02907"},{"id":"kipfVariationalGraphAutoEncoders2016","abstract":"We introduce the variational graph auto-encoder (VGAE), a framework for unsupervised learning on graph-structured data based on the variational auto-encoder (VAE). This model makes use of latent variables and is capable of learning interpretable latent representations for undirected graphs. We demonstrate this model using a graph convolutional network (GCN) encoder and a simple inner product decoder. Our model achieves competitive results on a link prediction task in citation networks. In contrast to most existing models for unsupervised learning on graph-structured data and link prediction, our model can naturally incorporate node features, which significantly improves predictive performance on a number of benchmark datasets.","accessed":{"date-parts":[["2024",5,14]]},"author":[{"family":"Kipf","given":"Thomas N."},{"family":"Welling","given":"Max"}],"citation-key":"kipfVariationalGraphAutoEncoders2016","DOI":"10.48550/arXiv.1611.07308","issued":{"date-parts":[["2016",11,21]]},"number":"arXiv:1611.07308","publisher":"arXiv","source":"arXiv.org","title":"Variational Graph Auto-Encoders","type":"article","URL":"http://arxiv.org/abs/1611.07308"},{"id":"kipfVariationalGraphAutoEncoders2016a","abstract":"We introduce the variational graph auto-encoder (VGAE), a framework for unsupervised learning on graph-structured data based on the variational auto-encoder (VAE). This model makes use of latent variables and is capable of learning interpretable latent representations for undirected graphs. We demonstrate this model using a graph convolutional network (GCN) encoder and a simple inner product decoder. Our model achieves competitive results on a link prediction task in citation networks. In contrast to most existing models for unsupervised learning on graph-structured data and link prediction, our model can naturally incorporate node features, which significantly improves predictive performance on a number of benchmark datasets.","accessed":{"date-parts":[["2025",5,9]]},"author":[{"family":"Kipf","given":"Thomas N."},{"family":"Welling","given":"Max"}],"citation-key":"kipfVariationalGraphAutoEncoders2016a","DOI":"10.48550/arXiv.1611.07308","issued":{"date-parts":[["2016",11,21]]},"note":"Read_Status: New\nRead_Status_Date: 2025-05-09T11:54:37.094Z","number":"arXiv:1611.07308","publisher":"arXiv","source":"arXiv.org","title":"Variational Graph Auto-Encoders","type":"article","URL":"http://arxiv.org/abs/1611.07308"},{"id":"kipfVariationalGraphAutoEncoders2016a","abstract":"We introduce the variational graph auto-encoder (VGAE), a framework for unsupervised learning on graph-structured data based on the variational auto-encoder (VAE). This model makes use of latent variables and is capable of learning interpretable latent representations for undirected graphs. We demonstrate this model using a graph convolutional network (GCN) encoder and a simple inner product decoder. Our model achieves competitive results on a link prediction task in citation networks. In contrast to most existing models for unsupervised learning on graph-structured data and link prediction, our model can naturally incorporate node features, which significantly improves predictive performance on a number of benchmark datasets.","accessed":{"date-parts":[["2025",5,9]]},"author":[{"family":"Kipf","given":"Thomas N."},{"family":"Welling","given":"Max"}],"citation-key":"kipfVariationalGraphAutoEncoders2016a","DOI":"10.48550/arXiv.1611.07308","issued":{"date-parts":[["2016",11,21]]},"title":"Variational Graph Auto-Encoders","type":"webpage","URL":"http://arxiv.org/abs/1611.07308"},{"id":"knuthAnalysisAlphabetaPruning1975","abstract":"The alpha-beta technique for searching game trees is analyzed, in an attempt to provide some insight into its behavior. The first portion of this paper is an expository presentation of the method together with a proof of its correctness and a historical discussion. The alpha-beta procedure is shown to be optimal in a certain sense, and bounds are obtained for its running time with various kinds of random data.","accessed":{"date-parts":[["2024",2,5]]},"author":[{"family":"Knuth","given":"Donald E."},{"family":"Moore","given":"Ronald W."}],"citation-key":"knuthAnalysisAlphabetaPruning1975","container-title":"Artificial Intelligence","container-title-short":"Artificial Intelligence","DOI":"10.1016/0004-3702(75)90019-3","ISSN":"0004-3702","issue":"4","issued":{"date-parts":[["1975",12,1]]},"page":"293–326","source":"ScienceDirect","title":"An analysis of alpha-beta pruning","type":"article-journal","URL":"https://www.sciencedirect.com/science/article/pii/0004370275900193","volume":"6"},{"id":"kolaczykStatisticalAnalysisNetwork2009","accessed":{"date-parts":[["2025",5,26]]},"author":[{"family":"Kolaczyk","given":"Eric D."}],"citation-key":"kolaczykStatisticalAnalysisNetwork2009","collection-title":"Springer Series in Statistics","DOI":"10.1007/978-0-387-88146-1","ISBN":"978-0-387-88145-4 978-0-387-88146-1","issued":{"date-parts":[["2009"]]},"language":"en","license":"https://www.springernature.com/gp/researchers/text-and-data-mining","note":"Read_Status: New\nRead_Status_Date: 2025-05-26T11:42:27.939Z","publisher":"Springer New York","publisher-place":"New York, NY","source":"DOI.org (Crossref)","title":"Statistical Analysis of Network Data: Methods and Models","title-short":"Statistical Analysis of Network Data","type":"book","URL":"https://link.springer.com/10.1007/978-0-387-88146-1"},{"id":"korotinNeuralOptimalTransport2023","abstract":"We present a novel neural-networks-based algorithm to compute optimal transport maps and plans for strong and weak transport costs. To justify the usage of neural networks, we prove that they are universal approximators of transport plans between probability distributions. We evaluate the performance of our optimal transport algorithm on toy examples and on the unpaired image-to-image translation.","accessed":{"date-parts":[["2025",6,11]]},"author":[{"family":"Korotin","given":"Alexander"},{"family":"Selikhanovych","given":"Daniil"},{"family":"Burnaev","given":"Evgeny"}],"citation-key":"korotinNeuralOptimalTransport2023","DOI":"10.48550/arXiv.2201.12220","issued":{"date-parts":[["2023",3,1]]},"note":"Read_Status: New\nRead_Status_Date: 2025-06-11T15:47:07.215Z","number":"arXiv:2201.12220","publisher":"arXiv","source":"arXiv.org","title":"Neural Optimal Transport","type":"article","URL":"http://arxiv.org/abs/2201.12220"},{"id":"krauseSkeletonCretaceousMammal2020","abstract":"The fossil record of mammaliaforms (mammals and their closest relatives) of the Mesozoic era from the southern supercontinent Gondwana is far less extensive than that from its northern counterpart, Laurasia1,2. Among Mesozoic mammaliaforms, Gondwanatheria is one of the most poorly known clades, previously represented by only a single cranium and isolated jaws and teeth1–5. As a result, the anatomy, palaeobiology and phylogenetic relationships of gondwanatherians remain unclear. Here we report the discovery of an articulated and very well-preserved skeleton of a gondwanatherian of the latest age (72.1–66 million years ago) of the Cretaceous period from Madagascar that we assign to a new genus and species, Adalatherium hui. To our knowledge, the specimen is the most complete skeleton of a Gondwanan Mesozoic mammaliaform that has been found, and includes the only postcranial material and ascending ramus of the dentary known for any gondwanatherian. A phylogenetic analysis including the new taxon recovers Gondwanatheria as the sister group to Multituberculata. The skeleton, which represents one of the largest of the Gondwanan Mesozoic mammaliaforms, is particularly notable for exhibiting many unique features in combination with features that are convergent on those of therian mammals. This uniqueness is consistent with a lineage history for A. hui of isolation on Madagascar for more than 20 million years.","accessed":{"date-parts":[["2025",3,10]]},"author":[{"family":"Krause","given":"David W."},{"family":"Hoffmann","given":"Simone"},{"family":"Hu","given":"Yaoming"},{"family":"Wible","given":"John R."},{"family":"Rougier","given":"Guillermo W."},{"family":"Kirk","given":"E. Christopher"},{"family":"Groenke","given":"Joseph R."},{"family":"Rogers","given":"Raymond R."},{"family":"Rossie","given":"James B."},{"family":"Schultz","given":"Julia A."},{"family":"Evans","given":"Alistair R."},{"family":"Koenigswald","given":"Wighart","non-dropping-particle":"von"},{"family":"Rahantarisoa","given":"Lydia J."}],"citation-key":"krauseSkeletonCretaceousMammal2020","container-title":"Nature","DOI":"10.1038/s41586-020-2234-8","ISSN":"1476-4687","issue":"7809","issued":{"date-parts":[["2020",5]]},"language":"en","license":"2020 The Author(s), under exclusive licence to Springer Nature Limited","page":"421–427","publisher":"Nature Publishing Group","source":"www.nature.com","title":"Skeleton of a Cretaceous mammal from Madagascar reflects long-term insularity","type":"article-journal","URL":"https://www.nature.com/articles/s41586-020-2234-8","volume":"581"},{"id":"kriegeSurveyGraphKernels2020","abstract":"Graph kernels have become an established and widely-used technique for solving classification tasks on graphs. This survey gives a comprehensive overview of techniques for kernel-based graph classification developed in the past 15 years. We describe and categorize graph kernels based on properties inherent to their design, such as the nature of their extracted graph features, their method of computation and their applicability to problems in practice. In an extensive experimental evaluation, we study the classification accuracy of a large suite of graph kernels on established benchmarks as well as new datasets. We compare the performance of popular kernels with several baseline methods and study the effect of applying a Gaussian RBF kernel to the metric induced by a graph kernel. In doing so, we find that simple baselines become competitive after this transformation on some datasets. Moreover, we study the extent to which existing graph kernels agree in their predictions (and prediction errors) and obtain a data-driven categorization of kernels as result. Finally, based on our experimental results, we derive a practitioner’s guide to kernel-based graph classification.","accessed":{"date-parts":[["2025",1,26]]},"author":[{"family":"Kriege","given":"Nils M."},{"family":"Johansson","given":"Fredrik D."},{"family":"Morris","given":"Christopher"}],"citation-key":"kriegeSurveyGraphKernels2020","container-title":"Applied Network Science","container-title-short":"Appl Netw Sci","DOI":"10.1007/s41109-019-0195-3","ISSN":"2364-8228","issue":"1","issued":{"date-parts":[["2020",1,14]]},"language":"en","page":"6","source":"Springer Link","title":"A survey on graph kernels","type":"article-journal","URL":"https://doi.org/10.1007/s41109-019-0195-3","volume":"5"},{"id":"krugerUnskilledUnawareIt","abstract":"In short, Study 1 revealed two effects of interest. First, although perceptions of ability were modestly correlated with actual ability, people tended to overestimate their ability relative to their peers. Second, and most important, those who performed particularly poorly relative to theirpeers were utterly unaware of this fact. Participants scoring in the bottom quartile on our humor test not only overestimated their percentile ranking, but they overestimated it by 46 percentile points. To be sure, they had an inkling that they were not as talented in this domain as were participants in the top quartile, as evidenced by the significant correlation between perceived and actual ability. However, that suspicion failed to anticipate the magnitude of their shortcomings.","author":[{"family":"Kruger","given":"Justin"},{"family":"Dunning","given":"David"}],"citation-key":"krugerUnskilledUnawareIt","language":"en","source":"Zotero","title":"Unskilled and Unaware of It: How Difficulties in Recognizing One's Own Incompetence Lead to Inflated Self-Assessments","type":"article-journal"},{"id":"kumpulainenYourBlockOur2024","abstract":"Stochastic Block Models (SBMs) are a popular approach to modeling single real-world graphs. The key idea of SBMs is to partition the vertices of the graph into blocks with similar edge densities within, as well as between different blocks. However, what if we are given not one but multiple graphs that are unaligned and of different sizes? How can we find out if these graphs share blocks with similar connectivity structures? In this paper, we propose the shared stochastic block modeling (SSBM) problem, in which we model n graphs using SBMs that share parameters of s blocks. We show that fitting an SSBM is NP-hard, and consider two approaches to fit good models in practice. In the first, we directly maximize the likelihood of the shared model using a Markov chain Monte Carlo algorithm. In the second, we first fit an SBM for each graph and then select which blocks to share. We propose an integer linear program to find the optimal shared blocks and to scale to large numbers of blocks, we propose a fast greedy algorithm. Through extensive empirical evaluation on synthetic and real-world data, we show that our methods work well in practice.","accessed":{"date-parts":[["2025",1,9]]},"author":[{"family":"Kumpulainen","given":"Iiro"},{"family":"Dalleiger","given":"Sebastian"},{"family":"Vreeken","given":"Jilles"},{"family":"Tatti","given":"Nikolaj"}],"citation-key":"kumpulainenYourBlockOur2024","DOI":"10.48550/arXiv.2412.15476","issued":{"date-parts":[["2024",12,20]]},"language":"en","number":"arXiv:2412.15476","publisher":"arXiv","source":"arXiv.org","title":"From your Block to our Block: How to Find Shared Structure between Stochastic Block Models over Multiple Graphs","title-short":"From your Block to our Block","type":"article","URL":"http://arxiv.org/abs/2412.15476"},{"id":"kumpulainenYourBlockOur2024","abstract":"Stochastic Block Models (SBMs) are a popular approach to modeling single real-world graphs. The key idea of SBMs is to partition the vertices of the graph into blocks with similar edge densities within, as well as between different blocks. However, what if we are given not one but multiple graphs that are unaligned and of different sizes? How can we find out if these graphs share blocks with similar connectivity structures? In this paper, we propose the shared stochastic block modeling (SSBM) problem, in which we model n graphs using SBMs that share parameters of s blocks. We show that fitting an SSBM is NP-hard, and consider two approaches to fit good models in practice. In the first, we directly maximize the likelihood of the shared model using a Markov chain Monte Carlo algorithm. In the second, we first fit an SBM for each graph and then select which blocks to share. We propose an integer linear program to find the optimal shared blocks and to scale to large numbers of blocks, we propose a fast greedy algorithm. Through extensive empirical evaluation on synthetic and real-world data, we show that our methods work well in practice.","accessed":{"date-parts":[["2025",1,9]]},"author":[{"family":"Kumpulainen","given":"Iiro"},{"family":"Dalleiger","given":"Sebastian"},{"family":"Vreeken","given":"Jilles"},{"family":"Tatti","given":"Nikolaj"}],"citation-key":"kumpulainenYourBlockOur2024","DOI":"10.48550/arXiv.2412.15476","issued":{"date-parts":[["2024",12,20]]},"language":"english","title":"From your Block to our Block: How to Find Shared Structure between Stochastic Block Models over Multiple Graphs","title-short":"From your Block to our Block","type":"webpage","URL":"http://arxiv.org/abs/2412.15476"},{"id":"kundakovicSexHormoneFluctuation2022","abstract":"Women are at twice the risk for anxiety and depression disorders as men are, although the underlying biological factors and mechanisms are largely unknown. In this review, we address this sex disparity at both the etiological and mechanistic level. We dissect the role of fluctuating sex hormones as a critical biological factor contributing to the increased depression and anxiety risk in women. We provide parallel evidence in humans and rodents that brain structure and function vary with naturally-cycling ovarian hormones. This female-unique brain plasticity and associated vulnerability are primarily driven by estrogen level changes. For the first time, we provide a sex hormone-driven molecular mechanism, namely chromatin organizational changes, that regulates neuronal gene expression and brain plasticity but may also prime the (epi)genome for psychopathology. Finally, we map out future directions including experimental and clinical studies that will facilitate novel sex- and gender-informed approaches to treat depression and anxiety disorders.","accessed":{"date-parts":[["2024",5,29]]},"author":[{"family":"Kundakovic","given":"Marija"},{"family":"Rocks","given":"Devin"}],"citation-key":"kundakovicSexHormoneFluctuation2022","container-title":"Frontiers in neuroendocrinology","container-title-short":"Front Neuroendocrinol","DOI":"10.1016/j.yfrne.2022.101010","ISSN":"0091-3022","issued":{"date-parts":[["2022",7]]},"page":"101010","PMCID":"PMC9715398","PMID":"35716803","source":"PubMed Central","title":"Sex hormone fluctuation and increased female risk for depression and anxiety disorders: from clinical evidence to molecular mechanisms","title-short":"Sex hormone fluctuation and increased female risk for depression and anxiety disorders","type":"article-journal","URL":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9715398/","volume":"66"},{"id":"kunegisLinkPredictionProblem2010","abstract":"We define and study the link prediction problem in bipartite networks, specializing general link prediction algorithms to the bipartite case. In a graph, a link prediction function of two vertices denotes the similarity or proximity of the vertices. Common link prediction functions for general graphs are defined using paths of length two between two nodes. Since in a bipartite graph adjacency vertices can only be connected by paths of odd lengths, these functions do not apply to bipartite graphs. Instead, a certain class of graph kernels (spectral transformation kernels) can be generalized to bipartite graphs when the positive-semidefinite kernel constraint is relaxed. This generalization is realized by the odd component of the underlying spectral transformation. This construction leads to several new link prediction pseudokernels such as the matrix hyperbolic sine, which we examine for rating graphs, authorship graphs, folksonomies, document–feature networks and other types of bipartite networks.","author":[{"family":"Kunegis","given":"Jérôme"},{"family":"De Luca","given":"Ernesto W."},{"family":"Albayrak","given":"Sahin"}],"citation-key":"kunegisLinkPredictionProblem2010","container-title":"Computational Intelligence for Knowledge-Based Systems Design","DOI":"10.1007/978-3-642-14049-5_39","editor":[{"family":"Hüllermeier","given":"Eyke"},{"family":"Kruse","given":"Rudolf"},{"family":"Hoffmann","given":"Frank"}],"ISBN":"978-3-642-14049-5","issued":{"date-parts":[["2010"]]},"language":"en","page":"380–389","publisher":"Springer","publisher-place":"Berlin, Heidelberg","source":"Springer Link","title":"The Link Prediction Problem in Bipartite Networks","type":"paper-conference"},{"id":"kuznetsovaLmerTestPackageTests2017","abstract":"One of the frequent questions by users of the mixed model function lmer of the lme4 package has been: How can I get p values for the F and t tests for objects returned by lmer? The lmerTest package extends the ‘lmerMod’ class of the lme4 package, by overloading the anova and summary functions by providing p values for tests for fixed effects. We have implemented the Satterthwaite’s method for approximating degrees of freedom for the t and F tests. We have also implemented the construction of Type I–III ANOVA tables. Furthermore, one may also obtain the summary as well as the anova table using the Kenward-Roger approximation for denominator degrees of freedom (based on the KRmodcomp function from the pbkrtest package). Some other convenient mixed model analysis tools such as a step method, that performs backward elimination of nonsignificant effects – both random and fixed, calculation of population means and multiple comparison tests together with plot facilities are provided by the package as well.","accessed":{"date-parts":[["2024",3,1]]},"author":[{"family":"Kuznetsova","given":"Alexandra"},{"family":"Brockhoff","given":"Per B."},{"family":"Christensen","given":"Rune H. B."}],"citation-key":"kuznetsovaLmerTestPackageTests2017","container-title":"Journal of Statistical Software","container-title-short":"J. Stat. Soft.","DOI":"10.18637/jss.v082.i13","ISSN":"1548-7660","issue":"13","issued":{"date-parts":[["2017"]]},"language":"en","source":"DOI.org (Crossref)","title":"<b>lmerTest</b> Package: Tests in Linear Mixed Effects Models","title-short":"<b>lmerTest</b> Package","type":"article-journal","URL":"http://www.jstatsoft.org/v82/i13/","volume":"82"},{"id":"lacosteCommonStructureDiscovery2025","abstract":"Bipartite networks are widely used to encode the ecological interactions. Being able to compare the organization of bipartite networks is a first step toward a better understanding of how environmental factors shape community structure and resilience. Yet current methods for structure detection in bipartite networks overlook shared patterns across collections of networks. We introduce the colBiSBM, a family of probabilistic models for collections of bipartite networks that extends the classical Latent Block Model (LBM). The proposed framework assumes that networks are independent realizations of a shared mesoscale structure, encoded through common inter-block connectivity parameters. We establish identifiability conditions for the different variants of colBiSBM and develop a variational EM algorithm for parameter estimation, coupled with an adaptation of the integrated classification likelihood (ICL) criterion for model selection. We demonstrate how our approach can be used to classify networks based on their topology or organization. Simulation studies highlight the ability of colBiSBM to recover common structures, improve clustering performance, and enhance link prediction by borrowing strength across networks. An application to plant–pollinator networks highlights how the method uncovers shared ecological roles and partitions networks into sub-collections with similar connectivity patterns. These results illustrate the methodological and practical advantages of joint modeling over separate network analyses in the study of bipartite systems.","accessed":{"date-parts":[["2026",3,17]]},"author":[{"family":"Lacoste","given":"Louis"},{"family":"Barbillon","given":"Pierre"},{"family":"Donnet","given":"Sophie"}],"citation-key":"lacosteCommonStructureDiscovery2025","DOI":"10.48550/arXiv.2512.01716","issued":{"date-parts":[["2025",12,1]]},"language":"en","note":"Read_Status: New\nRead_Status_Date: 2026-03-17T08:38:41.405Z","number":"arXiv:2512.01716","publisher":"arXiv","source":"arXiv.org","title":"Common Structure Discovery in Collections of Bipartite Networks: Application to Pollination Systems","title-short":"Common Structure Discovery in Collections of Bipartite Networks","type":"article","URL":"http://arxiv.org/abs/2512.01716"},{"id":"larousseDefinitionsBipartiBipartite","abstract":"biparti, bipartite - Définitions Français : Retrouvez la définition de biparti, bipartite, ainsi que les difficultés... - synonymes, homonymes, difficultés, citations.","accessed":{"date-parts":[["2023",6,17]]},"author":[{"family":"Larousse","given":"Éditions"}],"citation-key":"larousseDefinitionsBipartiBipartite","language":"fr","title":"Définitions : biparti, bipartite - Dictionnaire de français Larousse","title-short":"Définitions","type":"webpage","URL":"https://www.larousse.fr/dictionnaires/francais/biparti/9503"},{"id":"latoucheVariationalBayesianInference2012","abstract":"It is now widely accepted that knowledge can be acquired from networks by clustering their vertices according to the connection profiles. Many methods have been proposed and in this paper we concentrate on the Stochastic Block Model (SBM). The clustering of vertices and the estimation of SBM model parameters have been subject to previous work, and numerous inference strategies such as variational expectation maximization (EM) and classification EM have been proposed. However, SBM still suffers from a lack of criteria to estimate the number of components in the mixture. To our knowledge, only one model-based criterion, Integrated Complete-data Likelihood (ICL), has been derived for SBM in the literature. It relies on an asymptotic approximation of the integrated complete-data likelihood and recent studies have shown that it tends to be too conservative in the case of small networks. To tackle this issue, we propose a new criterion that we call Integrated Likelihood Variational Bayes (ILvb), based on a non-asymptotic approximation of the marginal likelihood. We describe how the criterion can be computed through a variational Bayes EM algorithm.","accessed":{"date-parts":[["2025",1,26]]},"author":[{"family":"Latouche","given":"P"},{"family":"Birmelé","given":"E"},{"family":"Ambroise","given":"C"}],"citation-key":"latoucheVariationalBayesianInference2012","container-title":"Statistical Modelling","DOI":"10.1177/1471082X1001200105","ISSN":"1471-082X","issue":"1","issued":{"date-parts":[["2012",2,1]]},"language":"en","page":"93–115","publisher":"SAGE Publications India","source":"SAGE Journals","title":"Variational Bayesian inference and complexity control for stochastic block models","type":"article-journal","URL":"https://doi.org/10.1177/1471082X1001200105","volume":"12"},{"id":"ledoitHoneyShrunkSample","abstract":"The central message of this paper is that nobody should be using the sample covariance matrix for the purpose of portfolio optimization. It contains estimation error of the kind most likely to perturb a mean-variance optimizer. In its place, we suggest using the matrix obtained from the sample covariance matrix through a transformation called shrinkage. This tends to pull the most extreme coefficients towards more central values, thereby systematically reducing estimation error where it matters most. Statistically, the challenge is to know the optimal shrinkage intensity, and we give the formula for that. Without changing any other step in the portfolio optimization process, we show on actual stock market data that shrinkage reduces tracking error relative to a benchmark index, and substantially increases the realized information ratio of the active portfolio manager.","author":[{"family":"Ledoit","given":"Olivier"},{"family":"Wolf","given":"Michael"}],"citation-key":"ledoitHoneyShrunkSample","language":"en","note":"Read_Status: New\nRead_Status_Date: 2025-12-09T10:32:52.780Z","source":"Zotero","title":"Honey, I Shrunk the Sample Covariance Matrix","type":"article-journal"},{"id":"legerBlockmodelsLatentStochastic2021","abstract":"Latent and Stochastic Block Model estimation by a Variational EM algorithm. Various probability distribution are provided (Bernoulli, Poisson...), with or without covariates.","accessed":{"date-parts":[["2024",11,4]]},"author":[{"family":"Leger","given":"Jean-Benoist"},{"family":"Barbillon","given":"Pierre"},{"family":"Chiquet","given":"Julien"}],"citation-key":"legerBlockmodelsLatentStochastic2021","issued":{"date-parts":[["2021",12,1]]},"license":"LGPL-2.1","source":"R-Packages","title":"blockmodels: Latent and Stochastic Block Model Estimation by a 'V-EM' Algorithm","title-short":"blockmodels","type":"software","URL":"https://cran.r-project.org/web/packages/blockmodels/index.html","version":"1.1.5"},{"id":"legerBlockmodelsLatentStochastic2021","abstract":"Latent and Stochastic Block Model estimation by a Variational EM algorithm. Various probability distribution are provided (Bernoulli, Poisson...), with or without covariates.","accessed":{"date-parts":[["2024",11,4]]},"author":[{"family":"Leger","given":"Jean-Benoist"},{"family":"Barbillon","given":"Pierre"},{"family":"Chiquet","given":"Julien"}],"citation-key":"legerBlockmodelsLatentStochastic2021","issued":{"date-parts":[["2021",12,1]]},"title":"Blockmodels: Latent and Stochastic Block Model Estimation by a 'V-EM' Algorithm","title-short":"Blockmodels","type":"software","URL":"https://cran.r-project.org/web/packages/blockmodels/index.html","version":"1.1.5"},{"id":"levy-leducNotesPourCours2024","author":[{"family":"Lévy-Leduc","given":"Céline"}],"citation-key":"levy-leducNotesPourCours2024","issued":{"date-parts":[["2024"]]},"language":"fr","source":"Zotero","title":"Notes pour le cours : “Méthodes de statistique en grande dimension pour l’analyse de données de biologie moléculaire”","type":"document"},{"id":"LinfrastructureMigale","accessed":{"date-parts":[["2024",2,2]]},"citation-key":"LinfrastructureMigale","title":"L’infrastructure de Migale","type":"webpage","URL":"https://documents.migale.inrae.fr/posts/tutorials/migale-infra/"},{"id":"llopis-belenguerSensitivityBipartiteNetwork2023","abstract":"Abstract Bipartite network analysis is a powerful tool to study the processes structuring interactions in ecological communities. In applying the method, it is assumed that the sampled interactions provide an accurate representation of the actual community. However, acquiring a representative sample may be difficult as not all species are equally abundant or easily identifiable. Two potential sampling issues can compromise the conclusions of bipartite network analyses: failure to capture the full range of interactions (sampling completeness) and use of a taxonomic level higher than species to evaluate the network (taxonomic resolution). We asked how commonly used descriptors of bipartite antagonistic communities (modularity, nestedness, connectance, and specialization [H2?]) are affected by reduced host sampling completeness, parasite taxonomic resolution, and their crossed effect, as they are likely to co-occur. We used a quantitative niche model to generate weighted bipartite networks that resembled natural host?parasite communities. The descriptors were more sensitive to uncertainty in parasite taxonomic resolution than to host sampling completeness. When only 10% of parasite taxonomic resolution was retained, modularity and specialization decreased by ~76% and ~12%, respectively, and nestedness and connectance increased by ~114% and ~345% respectively. The loss of taxonomic resolution led to a wide range of possible communities, which made it difficult to predict its effects on a given network. With regards to host sampling completeness, standardized nestedness, connectance, and specialization were robust, whereas modularity was sensitive (~30% decrease). The combination of both sampling issues had an additive effect on modularity. In communities with low effort for both sampling issues (50%?10% of sampling completeness and taxonomic resolution), estimators of modularity, and nestedness could not be distinguished from those of random assemblages. Thus, the categorical description of communities with low sampling effort (e.g., if a community is modular or not) should be done with caution. We recommend evaluating both sampling completeness and taxonomic certainty when conducting bipartite network analyses. Care should also be exercised when using nonrobust descriptors (the four descriptors for parasite taxonomic resolution; modularity for host sampling completeness) when sampling issues are likely to affect a dataset.","accessed":{"date-parts":[["2025",9,18]]},"author":[{"family":"Llopis-Belenguer","given":"Cristina"},{"family":"Balbuena","given":"Juan Antonio"},{"family":"Blasco-Costa","given":"Isabel"},{"family":"Karvonen","given":"Anssi"},{"family":"Sarabeev","given":"Volodimir"},{"family":"Jokela","given":"Jukka"}],"citation-key":"llopis-belenguerSensitivityBipartiteNetwork2023","container-title":"Ecology","DOI":"10.1002/ecy.3974","ISSN":"0012-9658","issue":"4","issued":{"date-parts":[["2023",4]]},"note":"Read_Status: New\nRead_Status_Date: 2025-09-18T14:55:12.700Z","page":"e3974","publisher":"John Wiley & Sons, Ltd","source":"esajournals.onlinelibrary.wiley.com (Atypon)","title":"Sensitivity of bipartite network analyses to incomplete sampling and taxonomic uncertainty","type":"article-journal","URL":"https://esajournals.onlinelibrary.wiley.com/doi/full/10.1002/ecy.3974","volume":"104"},{"id":"lozuponeQuantitativeQualitativeDiversity2007","abstract":"The assessment of microbial diversity and distribution is a major concern in environmental microbiology. There are two general approaches for measuring community diversity: quantitative measures, which use the abundance of each taxon, and qualitative measures, which use only the presence/absence of data. Quantitative measures are ideally suited to revealing community differences that are due to changes in relative taxon abundance (e.g., when a particular set of taxa flourish because a limiting nutrient source becomes abundant). Qualitative measures are most informative when communities differ primarily by what can live in them (e.g., at high temperatures), in part because abundance information can obscure significant patterns of variation in which taxa are present. We illustrate these principles using two 16S rRNA-based surveys of microbial populations and two phylogenetic measures of community β diversity: unweighted UniFrac, a qualitative measure, and weighted UniFrac, a new quantitative measure, which we have added to the UniFrac website (http://bmf.colorado.edu/unifrac ). These studies considered the relative influences of mineral chemistry, temperature, and geography on microbial community composition in acidic thermal springs in Yellowstone National Park and the influences of obesity and kinship on microbial community composition in the mouse gut. We show that applying qualitative and quantitative measures to the same data set can lead to dramatically different conclusions about the main factors that structure microbial diversity and can provide insight into the nature of community differences. We also demonstrate that both weighted and unweighted UniFrac measurements are robust to the methods used to build the underlying phylogeny.","accessed":{"date-parts":[["2025",11,7]]},"author":[{"family":"Lozupone","given":"Catherine A."},{"family":"Hamady","given":"Micah"},{"family":"Kelley","given":"Scott T."},{"family":"Knight","given":"Rob"}],"citation-key":"lozuponeQuantitativeQualitativeDiversity2007","container-title":"Applied and Environmental Microbiology","DOI":"10.1128/AEM.01996-06","issue":"5","issued":{"date-parts":[["2007",3]]},"note":"Read_Status: New\nRead_Status_Date: 2025-11-07T14:55:50.153Z","page":"1576–1585","publisher":"American Society for Microbiology","source":"journals.asm.org (Atypon)","title":"Quantitative and Qualitative β Diversity Measures Lead to Different Insights into Factors That Structure Microbial Communities","type":"article-journal","URL":"https://journals.asm.org/doi/10.1128/aem.01996-06","volume":"73"},{"id":"maatenVisualizingDataUsing2008","abstract":"We present a new technique called \"t-SNE\" that visualizes high-dimensional data by giving each datapoint a location in a two or three-dimensional map. The technique is a variation of Stochastic Neighbor Embedding (Hinton and Roweis, 2002) that is much easier to optimize, and produces significantly better visualizations by reducing the tendency to crowd points together in the center of the map. t-SNE is better than existing techniques at creating a single map that reveals structure at many different scales. This is particularly important for high-dimensional data that lie on several different, but related, low-dimensional manifolds, such as images ofobjects from multiple classes seen from multiple viewpoints. For visualizing the structure of very large data sets, we show how t-SNE can use random walks on neighborhood graphs to allow the implicit structure of all of the data to influence the way in which a subset of the data is displayed. We illustrate the performance of t-SNE on a wide variety of data sets and compare it with many other non-parametric visualization techniques, including Sammon mapping, Isomap, and Locally Linear Embedding. The visualizations produced by t-SNE are significantly better than those produced by the other techniques on almost all of the data sets.","accessed":{"date-parts":[["2024",5,20]]},"author":[{"family":"Maaten","given":"Laurens","dropping-particle":"van der"},{"family":"Hinton","given":"Geoffrey"}],"citation-key":"maatenVisualizingDataUsing2008","container-title":"Journal of Machine Learning Research","ISSN":"1533-7928","issue":"86","issued":{"date-parts":[["2008"]]},"page":"2579–2605","source":"jmlr.org","title":"Visualizing Data using t-SNE","type":"article-journal","URL":"http://jmlr.org/papers/v9/vandermaaten08a.html","volume":"9"},{"id":"maeldoreMaelDorePollination_networksScripts2020","abstract":"R scripts for Doré et al., 2020 - Relative effects of anthropogenic pressures, climate, and sampling design on the structure of pollination networks at the global scale","accessed":{"date-parts":[["2023",6,21]]},"author":[{"family":"MaelDore","given":""}],"citation-key":"maeldoreMaelDorePollination_networksScripts2020","DOI":"10.5281/ZENODO.4290503","issued":{"date-parts":[["2020",11,25]]},"license":"Open Access","publisher":"Zenodo","source":"DOI.org (Datacite)","title":"MaelDore/Pollination_networks: R scripts for Doré et al., 2020 - Relative effects of anthropogenic pressures, climate, and sampling design on the structure of pollination networks at the global scale","title-short":"MaelDore/Pollination_networks","type":"article-journal","URL":"https://zenodo.org/record/4290503"},{"id":"maHierarchicalTaxonomyAware2018","accessed":{"date-parts":[["2025",9,24]]},"author":[{"family":"Ma","given":"Jianxin"},{"family":"Cui","given":"Peng"},{"family":"Wang","given":"Xiao"},{"family":"Zhu","given":"Wenwu"}],"citation-key":"maHierarchicalTaxonomyAware2018","container-title":"Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","DOI":"10.1145/3219819.3220062","event-title":"KDD '18: The 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","ISBN":"978-1-4503-5552-0","issued":{"date-parts":[["2018",7,19]]},"language":"en","note":"Read_Status: New\nRead_Status_Date: 2025-09-24T08:22:06.308Z","page":"1920–1929","publisher":"ACM","publisher-place":"London United Kingdom","source":"DOI.org (Crossref)","title":"Hierarchical Taxonomy Aware Network Embedding","type":"paper-conference","URL":"https://dl.acm.org/doi/10.1145/3219819.3220062"},{"id":"mariadassouUncoveringLatentStructure2010","accessed":{"date-parts":[["2026",1,30]]},"author":[{"family":"Mariadassou","given":"Mahendra"},{"family":"Robin","given":"Stéphane"},{"family":"Vacher","given":"Corinne"}],"citation-key":"mariadassouUncoveringLatentStructure2010","container-title":"The Annals of Applied Statistics","container-title-short":"Ann. Appl. Stat.","DOI":"10.1214/10-AOAS361","ISSN":"1932-6157","issue":"2","issued":{"date-parts":[["2010",6,1]]},"language":"en","note":"Read_Status: New\nRead_Status_Date: 2026-01-30T14:15:30.804Z","source":"DOI.org (Crossref)","title":"Uncovering latent structure in valued graphs: A variational approach","title-short":"Uncovering latent structure in valued graphs","type":"article-journal","URL":"https://projecteuclid.org/journals/annals-of-applied-statistics/volume-4/issue-2/Uncovering-latent-structure-in-valued-graphs-A-variational-approach/10.1214/10-AOAS361.full","volume":"4"},{"id":"mary-huardIntroductionAuCritere","abstract":"In this article we propose a discussion on the Bayesian model selection criterion BIC (Bayesian Information Criterion).In order to understand its behaviour, we describe the steps of its construction as well as the hypotheses required for its application and the approximations needed.Relying on the notion of quasi-true model, we explain the « dimension-consistency » property of BIC.Finally we show the basic differences between BIC and AIC via the comparison of their respective properties.","author":[{"family":"Mary-Huard","given":"Tristan"}],"citation-key":"mary-huardIntroductionAuCritere","language":"fr","note":"Read_Status: New\nRead_Status_Date: 2025-05-27T08:40:45.179Z","source":"Zotero","title":"Une introduction au critère BIC: fondements théoriques et interprétation","type":"article-journal"},{"id":"matchadoNetworkAnalysisMethods2021","abstract":"Microorganisms including bacteria, fungi, viruses, protists and archaea live as communities in complex and contiguous environments. They engage in numerous inter- and intra- kingdom interactions which can be inferred from microbiome profiling data. In particular, network-based approaches have proven helpful in deciphering complex microbial interaction patterns. Here we give an overview of state-of-the-art methods to infer intra-kingdom interactions ranging from simple correlation- to complex conditional dependence-based methods. We highlight common biases encountered in microbial profiles and discuss mitigation strategies employed by different tools and their trade-off with increased computational complexity. Finally, we discuss current limitations that motivate further method development to infer inter-kingdom interactions and to robustly and comprehensively characterize microbial environments in the future.","accessed":{"date-parts":[["2024",5,16]]},"author":[{"family":"Matchado","given":"Monica Steffi"},{"family":"Lauber","given":"Michael"},{"family":"Reitmeier","given":"Sandra"},{"family":"Kacprowski","given":"Tim"},{"family":"Baumbach","given":"Jan"},{"family":"Haller","given":"Dirk"},{"family":"List","given":"Markus"}],"citation-key":"matchadoNetworkAnalysisMethods2021","container-title":"Computational and Structural Biotechnology Journal","container-title-short":"Computational and Structural Biotechnology Journal","DOI":"10.1016/j.csbj.2021.05.001","ISSN":"2001-0370","issued":{"date-parts":[["2021",1,1]]},"page":"2687–2698","source":"ScienceDirect","title":"Network analysis methods for studying microbial communities: A mini review","title-short":"Network analysis methods for studying microbial communities","type":"article-journal","URL":"https://www.sciencedirect.com/science/article/pii/S2001037021001823","volume":"19"},{"id":"matchadoNetworkAnalysisMethods2021a","abstract":"Microorganisms including bacteria, fungi, viruses, protists and archaea live as communities in complex and contiguous environments. They engage in numerous inter- and intra- kingdom interactions which can be inferred from microbiome profiling data. In particular, network-based approaches have proven helpful in deciphering complex microbial interaction patterns. Here we give an overview of state-of-the-art methods to infer intra-kingdom interactions ranging from simple correlation- to complex conditional dependence-based methods. We highlight common biases encountered in microbial profiles and discuss mitigation strategies employed by different tools and their trade-off with increased computational complexity. Finally, we discuss current limitations that motivate further method development to infer inter-kingdom interactions and to robustly and comprehensively characterize microbial environments in the future.","accessed":{"date-parts":[["2025",5,6]]},"author":[{"family":"Matchado","given":"Monica Steffi"},{"family":"Lauber","given":"Michael"},{"family":"Reitmeier","given":"Sandra"},{"family":"Kacprowski","given":"Tim"},{"family":"Baumbach","given":"Jan"},{"family":"Haller","given":"Dirk"},{"family":"List","given":"Markus"}],"citation-key":"matchadoNetworkAnalysisMethods2021a","container-title":"Computational and Structural Biotechnology Journal","container-title-short":"Computational and Structural Biotechnology Journal","DOI":"10.1016/j.csbj.2021.05.001","ISSN":"2001-0370","issued":{"date-parts":[["2021",1,1]]},"note":"Read_Status: New\nRead_Status_Date: 2025-05-07T07:43:04.957Z","page":"2687–2698","source":"ScienceDirect","title":"Network analysis methods for studying microbial communities: A mini review","title-short":"Network analysis methods for studying microbial communities","type":"article-journal","URL":"https://www.sciencedirect.com/science/article/pii/S2001037021001823","volume":"19"},{"id":"matchadoNetworkAnalysisMethods2021b","abstract":"Microorganisms including bacteria, fungi, viruses, protists and archaea live as communities in complex and contiguous environments. They engage in numerous inter- and intra- kingdom interactions which can be inferred from microbiome profiling data. In particular, network-based approaches have proven helpful in deciphering complex microbial interaction patterns. Here we give an overview of state-of-the-art methods to infer intra-kingdom interactions ranging from simple correlation- to complex conditional dependence-based methods. We highlight common biases encountered in microbial profiles and discuss mitigation strategies employed by different tools and their trade-off with increased computational complexity. Finally, we discuss current limitations that motivate further method development to infer inter-kingdom interactions and to robustly and comprehensively characterize microbial environments in the future.","accessed":{"date-parts":[["2025",6,16]]},"author":[{"family":"Matchado","given":"Monica Steffi"},{"family":"Lauber","given":"Michael"},{"family":"Reitmeier","given":"Sandra"},{"family":"Kacprowski","given":"Tim"},{"family":"Baumbach","given":"Jan"},{"family":"Haller","given":"Dirk"},{"family":"List","given":"Markus"}],"citation-key":"matchadoNetworkAnalysisMethods2021b","container-title":"Computational and Structural Biotechnology Journal","container-title-short":"Computational and Structural Biotechnology Journal","DOI":"10.1016/j.csbj.2021.05.001","ISSN":"2001-0370","issued":{"date-parts":[["2021",1,1]]},"note":"Read_Status: New\nRead_Status_Date: 2025-06-16T16:18:09.496Z","page":"2687–2698","source":"ScienceDirect","title":"Network analysis methods for studying microbial communities: A mini review","title-short":"Network analysis methods for studying microbial communities","type":"article-journal","URL":"https://www.sciencedirect.com/science/article/pii/S2001037021001823","volume":"19"},{"id":"matiasStatisticalClusteringTemporal2016","abstract":"Statistical node clustering in discrete time dynamic networks is an emerging field that raises many challenges. Here, we explore statistical properties and frequentist inference in a model that combines a stochastic block model (SBM) for its static part with independent Markov chains for the evolution of the nodes groups through time. We model binary data as well as weighted dynamic random graphs (with discrete or continuous edges values). Our approach, motivated by the importance of controlling for label switching issues across the different time steps, focuses on detecting groups characterized by a stable within group connectivity behavior. We study identifiability of the model parameters, propose an inference procedure based on a variational expectation maximization algorithm as well as a model selection criterion to select for the number of groups. We carefully discuss our initialization strategy which plays an important role in the method and compare our procedure with existing ones on synthetic datasets. We also illustrate our approach on dynamic contact networks, one of encounters among high school students and two others on animal interactions. An implementation of the method is available as a R package called dynsbm.","accessed":{"date-parts":[["2025",3,1]]},"author":[{"family":"Matias","given":"Catherine"},{"family":"Miele","given":"Vincent"}],"citation-key":"matiasStatisticalClusteringTemporal2016","issued":{"date-parts":[["2016",6,22]]},"number":"arXiv:1506.07464","source":"arXiv.org","title":"Statistical clustering of temporal networks through a dynamic stochastic block model","type":"article","URL":"http://arxiv.org/abs/1506.07464"},{"id":"matiasStatisticalClusteringTemporal2017","abstract":"Statistical node clustering in discrete time dynamic networks is an emerging field that raises many challenges. Here, we explore statistical properties and frequentist inference in a model that combines a stochastic block model for its static part with independent Markov chains for the evolution of the nodes groups through time. We model binary data as well as weighted dynamic random graphs (with discrete or continuous edges values). Our approach, motivated by the importance of controlling for label switching issues across the different time steps, focuses on detecting groups characterized by a stable within-group connectivity behaviour. We study identifiability of the model parameters and propose an inference procedure based on a variational expectation–maximization algorithm as well as a model selection criterion to select the number of groups. We carefully discuss our initialization strategy which plays an important role in the method and we compare our procedure with existing procedures on synthetic data sets. We also illustrate our approach on dynamic contact networks: one of encounters between high school students and two others on animal interactions. An implementation of the method is available as an R package called dynsbm.","accessed":{"date-parts":[["2025",9,19]]},"author":[{"family":"Matias","given":"Catherine"},{"family":"Miele","given":"Vincent"}],"citation-key":"matiasStatisticalClusteringTemporal2017","container-title":"Journal of the Royal Statistical Society Series B: Statistical Methodology","container-title-short":"J. R. Stat. Soc. Ser. B. Stat. Methodol.","DOI":"10.1111/rssb.12200","ISSN":"1369-7412","issue":"4","issued":{"date-parts":[["2017",9,1]]},"note":"Read_Status: New\nRead_Status_Date: 2025-09-19T14:13:35.038Z","page":"1119–1141","source":"Silverchair","title":"Statistical Clustering of Temporal Networks Through a Dynamic Stochastic Block Model","type":"article-journal","URL":"https://doi.org/10.1111/rssb.12200","volume":"79"},{"id":"matiasStatisticalClusteringTemporal2017","abstract":"Statistical node clustering in discrete time dynamic networks is an emerging field that raises many challenges. Here, we explore statistical properties and frequentist inference in a model that combines a stochastic block model for its static part with independent Markov chains for the evolution of the nodes groups through time. We model binary data as well as weighted dynamic random graphs (with discrete or continuous edges values). Our approach, motivated by the importance of controlling for label switching issues across the different time steps, focuses on detecting groups characterized by a stable within-group connectivity behaviour. We study identifiability of the model parameters and propose an inference procedure based on a variational expectation–maximization algorithm as well as a model selection criterion to select the number of groups. We carefully discuss our initialization strategy which plays an important role in the method and we compare our procedure with existing procedures on synthetic data sets. We also illustrate our approach on dynamic contact networks: one of encounters between high school students and two others on animal interactions. An implementation of the method is available as an R package called dynsbm.","accessed":{"date-parts":[["2025",9,19]]},"author":[{"family":"Matias","given":"Catherine"},{"family":"Miele","given":"Vincent"}],"citation-key":"matiasStatisticalClusteringTemporal2017","container-title":"Journal of the Royal Statistical Society. Series B. Statistical Methodology","container-title-short":"J. R. Stat. Soc. Ser. B. Stat. Methodol.","DOI":"10.1111/rssb.12200","ISSN":"1369-7412","issue":"4","issued":{"date-parts":[["2017",9,1]]},"page":"1119–1141","title":"Statistical Clustering of Temporal Networks Through a Dynamic Stochastic Block Model","type":"article-journal","URL":"https://doi.org/10.1111/rssb.12200","volume":"79"},{"id":"mazeletUnsupervisedLearningOptimal","abstract":"Optimal transport between graphs, based on Gromov-Wasserstein and other extensions, is a powerful tool for comparing and aligning graph structures. However, solving the associated non-convex optimization problems is computationally expensive, which limits the scalability of these methods to large graphs. In this work, we present Unbalanced Learning of Optimal Transport (ULOT), a deep learning method that predicts optimal transport plans between two graphs. Our method is trained by minimizing the fused unbalanced Gromov-Wasserstein (FUGW) loss. We propose a novel neural architecture with cross-attention that is conditioned on the FUGW tradeoff hyperparameters. We evaluate ULOT on synthetic stochastic block model (SBM) graphs and on real cortical surface data obtained from fMRI. ULOT predicts transport plans with competitive loss up to two orders of magnitude faster than classical solvers. Furthermore, the predicted plan can be used as a warm start for classical solvers to accelerate their convergence. Finally, the predicted transport plan is fully differentiable with respect to the graph inputs and FUGW hyperparameters, enabling the optimization of functionals of the ULOT plan.","author":[{"family":"Mazelet","given":"Sonia"},{"family":"Flamary","given":"Rémi"},{"family":"Thirion","given":"Bertrand"}],"citation-key":"mazeletUnsupervisedLearningOptimal","language":"en","note":"Read_Status: New\nRead_Status_Date: 2025-06-11T09:08:09.864Z","source":"Zotero","title":"Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs","type":"article-journal"},{"id":"meinshausenStabilitySelection2010","abstract":"Summary. Estimation of structure, such as in variable selection, graphical modelling or cluster analysis, is notoriously difficult, especially for high dimensional data. We introduce stability selection. It is based on subsampling in combination with (high dimensional) selection algorithms. As such, the method is extremely general and has a very wide range of applicability. Stability selection provides finite sample control for some error rates of false discoveries and hence a transparent principle to choose a proper amount of regularization for structure estimation. Variable selection and structure estimation improve markedly for a range of selection methods if stability selection is applied. We prove for the randomized lasso that stability selection will be variable selection consistent even if the necessary conditions for consistency of the original lasso method are violated. We demonstrate stability selection for variable selection and Gaussian graphical modelling, using real and simulated data.","accessed":{"date-parts":[["2024",2,8]]},"author":[{"family":"Meinshausen","given":"Nicolai"},{"family":"Bühlmann","given":"Peter"}],"citation-key":"meinshausenStabilitySelection2010","container-title":"Journal of the Royal Statistical Society: Series B (Statistical Methodology)","DOI":"10.1111/j.1467-9868.2010.00740.x","ISSN":"1467-9868","issue":"4","issued":{"date-parts":[["2010"]]},"language":"en","license":"© 2010 Royal Statistical Society","page":"417–473","source":"Wiley Online Library","title":"Stability selection","type":"article-journal","URL":"https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1467-9868.2010.00740.x","volume":"72"},{"id":"meleardModelesAleatoiresEcologie2016","accessed":{"date-parts":[["2024",2,26]]},"author":[{"family":"Méléard","given":"Sylvie"}],"citation-key":"meleardModelesAleatoiresEcologie2016","collection-title":"Mathématiques et Applications","DOI":"10.1007/978-3-662-49455-4","ISBN":"978-3-662-49454-7 978-3-662-49455-4","issued":{"date-parts":[["2016"]]},"language":"fr","publisher":"Springer Berlin Heidelberg","publisher-place":"Berlin, Heidelberg","source":"DOI.org (Crossref)","title":"Modèles aléatoires en Ecologie et Evolution","type":"book","URL":"http://link.springer.com/10.1007/978-3-662-49455-4","volume":"77"},{"id":"michalska-smithTellingEcologicalNetworks2019","abstract":"Ecologists have been compiling ecological networks for over a century, detailing the interactions between species in a variety of ecosystems. To this end, they have built networks for mutualistic (e.g., pollination, seed dispersal) as well as antagonistic (e.g., herbivory, parasitism) interactions. The type of interaction being represented is believed to be reflected in the structure of the network, which would differ substantially between mutualistic and antagonistic networks. Here, we put this notion to the test by attempting to determine the type of interaction represented in a network based solely on its structure. We find that, although it is easy to separate different kinds of nonecological networks, ecological networks display much structural variation, making it difficult to distinguish between mutualistic and antagonistic interactions. We therefore frame the problem as a challenge for the community of scientists interested in computational biology and machine learning. We discuss the features a good solution to this problem should possess and the obstacles that need to be overcome to achieve this goal.","accessed":{"date-parts":[["2025",4,11]]},"author":[{"family":"Michalska-Smith","given":"Matthew J."},{"family":"Allesina","given":"Stefano"}],"citation-key":"michalska-smithTellingEcologicalNetworks2019","container-title":"PLOS Computational Biology","container-title-short":"PLoS Comput Biol","DOI":"10.1371/journal.pcbi.1007076","editor":[{"family":"Bollenbach","given":"Tobias"}],"ISSN":"1553-7358","issue":"6","issued":{"date-parts":[["2019",6,27]]},"language":"en","page":"e1007076","source":"DOI.org (Crossref)","title":"Telling ecological networks apart by their structure: A computational challenge","title-short":"Telling ecological networks apart by their structure","type":"article-journal","URL":"https://dx.plos.org/10.1371/journal.pcbi.1007076","volume":"15"},{"id":"michalska-smithTellingEcologicalNetworks2019","abstract":"Ecologists have been compiling ecological networks for over a century, detailing the interactions between species in a variety of ecosystems. To this end, they have built networks for mutualistic (e.g., pollination, seed dispersal) as well as antagonistic (e.g., herbivory, parasitism) interactions. The type of interaction being represented is believed to be reflected in the structure of the network, which would differ substantially between mutualistic and antagonistic networks. Here, we put this notion to the test by attempting to determine the type of interaction represented in a network based solely on its structure. We find that, although it is easy to separate different kinds of nonecological networks, ecological networks display much structural variation, making it difficult to distinguish between mutualistic and antagonistic interactions. We therefore frame the problem as a challenge for the community of scientists interested in computational biology and machine learning. We discuss the features a good solution to this problem should possess and the obstacles that need to be overcome to achieve this goal.","accessed":{"date-parts":[["2025",4,11]]},"author":[{"family":"Michalska-Smith","given":"Matthew J."},{"family":"Allesina","given":"Stefano"}],"citation-key":"michalska-smithTellingEcologicalNetworks2019","container-title":"PLoS computational biology","container-title-short":"PLoS Comput Biol","DOI":"10.1371/journal.pcbi.1007076","editor":[{"family":"Bollenbach","given":"Tobias"}],"ISSN":"1553-7358","issue":"6","issued":{"date-parts":[["2019",6,27]]},"language":"english","page":"e1007076","title":"Telling ecological networks apart by their structure: A computational challenge","title-short":"Telling ecological networks apart by their structure","type":"article-journal","URL":"https://dx.plos.org/10.1371/journal.pcbi.1007076","volume":"15"},{"id":"MigalePlatformMigale","accessed":{"date-parts":[["2024",2,2]]},"citation-key":"MigalePlatformMigale","title":"Migale platform | Migale","type":"webpage","URL":"https://migale.inrae.fr/"},{"id":"minkaOldNewMatrix","author":[{"family":"Minka","given":"Thomas"}],"citation-key":"minkaOldNewMatrix","note":"Read_Status: New\nRead_Status_Date: 2025-10-01T13:41:26.740Z","title":"Old and New Matrix Algebra Useful for Statistics","type":"book"},{"id":"MonotheticAnalysisProgram1990","abstract":"The prelims comprise: Short Description of the Method How to Use the Program MONA Examples More on the Algorithm and the Program Related Methods and References","accessed":{"date-parts":[["2024",9,13]]},"citation-key":"MonotheticAnalysisProgram1990","container-title":"Finding Groups in Data","DOI":"10.1002/9780470316801.ch7","ISBN":"978-0-470-31680-1","issued":{"date-parts":[["1990"]]},"language":"en","page":"280–311","publisher":"John Wiley & Sons, Ltd","section":"7","source":"Wiley Online Library","title":"Monothetic Analysis (Program MONA)","type":"chapter","URL":"https://onlinelibrary.wiley.com/doi/abs/10.1002/9780470316801.ch7"},{"id":"Morton2021.11.09.467939","abstract":"Estimating microbe-microbe interactions is critical for understanding the ecological laws governing microbial communities. Rapidly decreasing sequencing costs have promised new opportunities to estimate microbe-microbe interactions across thousands of uncultured, unknown microbes. However, typical microbiome datasets are very high dimensional and accurate estimation of microbial correlations requires tens of thousands of samples, exceeding the computational capabilities of existing methodologies. Furthermore, the vast majority of microbiome studies collect compositional metagenomics data which enforces a negative bias when computing microbe-microbe correlations. The Multinomial Logistic Normal (MLN) distribution has been shown to be effective at inferring microbe-microbe correlations, however scalable Bayesian inference of these distributions has remained elusive. Here, we show that carefully constructed Variational Autoencoders (VAEs) augmented with the Isometric Log-ratio (ILR) transform can estimate low-rank MLN distributions thousands of times faster than existing methods. These VAEs can be trained on tens of thousands of samples, enabling co-occurrence inference across tens of thousands of microbes without regularization. The latent embedding distances computed from these VAEs are competitive with existing beta-diversity methods across a variety of mouse and human microbiome classification and regression tasks, with notable improvements on longitudinal studies.Competing Interest StatementThe authors have declared no competing interest.","author":[{"family":"Morton","given":"James T."},{"family":"Silverman","given":"Justin"},{"family":"Tikhonov","given":"Gleb"},{"family":"Lähdesmäki","given":"Harri"},{"family":"Bonneau","given":"Rich"}],"citation-key":"Morton2021.11.09.467939","container-title":"bioRxiv : the preprint server for biology","container-title-short":"bioRxiv","DOI":"10.1101/2021.11.09.467939","issued":{"date-parts":[["2021"]]},"note":"Read_Status: New\nRead_Status_Date: 2025-06-30T14:17:29.518Z","publisher":"Cold Spring Harbor Laboratory","title":"Scalable estimation of microbial co-occurrence networks with Variational Autoencoders","type":"article-journal","URL":"https://www.biorxiv.org/content/early/2021/11/11/2021.11.09.467939"},{"id":"mortonScalableEstimationMicrobial2021","abstract":"Estimating microbe-microbe interactions is critical for understanding the ecological laws governing microbial communities. Rapidly decreasing sequencing costs have promised new opportunities to estimate microbe-microbe interactions across thousands of uncultured, unknown microbes. However, typical microbiome datasets are very high dimensional and accurate estimation of microbial correlations requires tens of thousands of samples, exceeding the computational capabilities of existing methodologies. Furthermore, the vast majority of microbiome studies collect compositional metagenomics data which enforces a negative bias when computing microbe-microbe correlations. The Multinomial Logistic Normal (MLN) distribution has been shown to be effective at inferring microbe-microbe correlations, however scalable Bayesian inference of these distributions has remained elusive. Here, we show that carefully constructed Variational Autoencoders (VAEs) augmented with the Isometric Log-ratio (ILR) transform can estimate low-rank MLN distributions thousands of times faster than existing methods. These VAEs can be trained on tens of thousands of samples, enabling co-occurrence inference across tens of thousands of microbes without regularization. The latent embedding distances computed from these VAEs are competitive with existing beta-diversity methods across a variety of mouse and human microbiome classification and regression tasks, with notable improvements on longitudinal studies.","accessed":{"date-parts":[["2025",7,1]]},"author":[{"family":"Morton","given":"James T."},{"family":"Silverman","given":"Justin"},{"family":"Tikhonov","given":"Gleb"},{"family":"Lähdesmäki","given":"Harri"},{"family":"Bonneau","given":"Rich"}],"citation-key":"mortonScalableEstimationMicrobial2021","DOI":"10.1101/2021.11.09.467939","issued":{"date-parts":[["2021",11,11]]},"language":"en","license":"© 2021, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution 4.0 International), CC BY 4.0, as described at http://creativecommons.org/licenses/by/4.0/","note":"Read_Status: New\nRead_Status_Date: 2025-07-01T07:35:21.948Z","page":"2021.11.09.467939","publisher":"bioRxiv","section":"New Results","source":"bioRxiv","title":"Scalable estimation of microbial co-occurrence networks with Variational Autoencoders","type":"article","URL":"https://www.biorxiv.org/content/10.1101/2021.11.09.467939v1"},{"id":"mukherjeeFoodWebDefinition2023","abstract":"What is a food web in biology. How does it work. Learn its different levels and types with an example and a simple labeled diagram.","accessed":{"date-parts":[["2025",9,21]]},"author":[{"family":"Mukherjee","given":"Santanu"}],"citation-key":"mukherjeeFoodWebDefinition2023","container-title":"Science Facts","issued":{"date-parts":[["2023",1,4]]},"language":"en-US","note":"Read_Status: New\nRead_Status_Date: 2025-09-23T11:03:33.193Z","title":"Food Web – Definition, Trophic Levels, Types, and Example","type":"post-weblog","URL":"https://www.sciencefacts.net/food-web.html"},{"id":"mukherjeeTrophicLevelDefinition2023","abstract":"What is a trophic level. How many are there. How much energy is transferred between them. How much energy is lost at each level. Learn a few examples with a diagram.","accessed":{"date-parts":[["2025",9,21]]},"author":[{"family":"Mukherjee","given":"Santanu"}],"citation-key":"mukherjeeTrophicLevelDefinition2023","container-title":"Science Facts","issued":{"date-parts":[["2023",1,27]]},"language":"en-US","note":"Read_Status: New\nRead_Status_Date: 2025-09-23T11:03:32.947Z","title":"Trophic Level - Definition, Examples, and Diagram","type":"post-weblog","URL":"https://www.sciencefacts.net/trophic-level.html"},{"id":"nairSuragnairAlphazerogeneral2024","abstract":"A clean implementation based on AlphaZero for any game in any framework + tutorial + Othello/Gobang/TicTacToe/Connect4 and more","accessed":{"date-parts":[["2024",2,19]]},"author":[{"family":"Nair","given":"Surag"}],"citation-key":"nairSuragnairAlphazerogeneral2024","genre":"Jupyter Notebook","issued":{"date-parts":[["2024",2,17]]},"license":"MIT","original-date":{"date-parts":[["2017",12,1]]},"source":"GitHub","title":"suragnair/alpha-zero-general","type":"software","URL":"https://github.com/suragnair/alpha-zero-general"},{"id":"nennaLecture1Monge","author":[{"family":"Nenna","given":"Luca"}],"citation-key":"nennaLecture1Monge","language":"en","note":"Read_Status: New\nRead_Status_Date: 2025-06-13T09:24:13.832Z","source":"Zotero","title":"Lecture 1 Monge and Kantorovich problems: from primal to dual","type":"article-journal"},{"id":"nennaLecture2Entropic","author":[{"family":"Nenna","given":"Luca"}],"citation-key":"nennaLecture2Entropic","language":"en","note":"Read_Status: New\nRead_Status_Date: 2025-06-11T16:06:28.547Z","source":"Zotero","title":"Lecture 2: Entropic Optimal Transport","type":"article-journal"},{"id":"newmanFindingCommunityStructure2006","accessed":{"date-parts":[["2024",9,9]]},"author":[{"family":"Newman","given":"M. E. J."}],"citation-key":"newmanFindingCommunityStructure2006","container-title":"Physical Review E","container-title-short":"Phys. Rev. E","DOI":"10.1103/PhysRevE.74.036104","ISSN":"1539-3755, 1550-2376","issue":"3","issued":{"date-parts":[["2006",9,11]]},"language":"en","license":"http://link.aps.org/licenses/aps-default-license","page":"036104","source":"DOI.org (Crossref)","title":"Finding community structure in networks using the eigenvectors of matrices","type":"article-journal","URL":"https://link.aps.org/doi/10.1103/PhysRevE.74.036104","volume":"74"},{"id":"ngSpectralClusteringAnalysis2001","abstract":"Despite many empirical successes of spectral clustering methods(cid:173) algorithms that cluster points using eigenvectors of matrices de(cid:173) rived from the data- there are several unresolved issues. First, there are a wide variety of algorithms that use the eigenvectors in slightly different ways. Second, many of these algorithms have no proof that they will actually compute a reasonable clustering. In this paper, we present a simple spectral clustering algorithm that can be implemented using a few lines of Matlab. Using tools from matrix perturbation theory, we analyze the algorithm, and give conditions under which it can be expected to do well. We also show surprisingly good experimental results on a number of challenging clustering problems.","accessed":{"date-parts":[["2025",10,8]]},"author":[{"family":"Ng","given":"Andrew"},{"family":"Jordan","given":"Michael"},{"family":"Weiss","given":"Yair"}],"citation-key":"ngSpectralClusteringAnalysis2001","container-title":"Advances in Neural Information Processing Systems","issued":{"date-parts":[["2001"]]},"note":"Read_Status: New\nRead_Status_Date: 2025-10-08T12:47:34.666Z","publisher":"MIT Press","source":"Neural Information Processing Systems","title":"On Spectral Clustering: Analysis and an algorithm","title-short":"On Spectral Clustering","type":"paper-conference","URL":"https://papers.nips.cc/paper_files/paper/2001/hash/801272ee79cfde7fa5960571fee36b9b-Abstract.html","volume":"14"},{"id":"nguenNetworkTwosampleTest2024","abstract":"We consider the two-sample testing problem for networks, where the goal is to determine whether two sets of networks originated from the same stochastic model. Assuming no vertex correspondence and allowing for different numbers of nodes, we address a fundamental network testing problem that goes beyond simple adjacency matrix comparisons. We adopt the stochastic block model (SBM) for network distributions, due to their interpretability and the potential to approximate more general models. The lack of meaningful node labels and vertex correspondence translate to a graph matching challenge when developing a test for SBMs. We introduce an efficient algorithm to match estimated network parameters, allowing us to properly combine and contrast information within and across samples, leading to a powerful test. We show that the matching algorithm, and the overall test are consistent, under mild conditions on the sparsity of the networks and the sample sizes, and derive a chi-squared asymptotic null distribution for the test. Through a mixture of theoretical insights and empirical validations, including experiments with both synthetic and real-world data, this study advances robust statistical inference for complex network data.","accessed":{"date-parts":[["2024",6,17]]},"author":[{"family":"Nguen","given":"Chung Kyong"},{"family":"Padilla","given":"Oscar Hernan Madrid"},{"family":"Amini","given":"Arash A."}],"citation-key":"nguenNetworkTwosampleTest2024","DOI":"10.48550/arXiv.2406.06014","issued":{"date-parts":[["2024",6,10]]},"number":"arXiv:2406.06014","publisher":"arXiv","source":"arXiv.org","title":"Network two-sample test for block models","type":"article","URL":"http://arxiv.org/abs/2406.06014"},{"id":"nierIntroductionTopologie","author":[{"family":"Nier","given":"Francis"},{"family":"Iftimie","given":"Dragos"}],"citation-key":"nierIntroductionTopologie","language":"fr","note":"Read_Status: New\nRead_Status_Date: 2025-10-03T14:07:07.662Z","source":"Zotero","title":"Introduction `a la Topologie","type":"article-journal"},{"id":"oehlertNoteDeltaMethod1992","abstract":"The delta method is an intuitive technique for approximating the moments of functions of random variables. This note reviews the delta method and conditions under which delta-method approximate moments are accurate.","accessed":{"date-parts":[["2025",6,24]]},"author":[{"family":"Oehlert","given":"Gary W."}],"citation-key":"oehlertNoteDeltaMethod1992","container-title":"The American Statistician","DOI":"10.2307/2684406","ISSN":"0003-1305","issue":"1","issued":{"date-parts":[["1992"]]},"note":"Read_Status: New\nRead_Status_Date: 2025-06-30T14:14:54.333Z","page":"27–29","publisher":"[American Statistical Association, Taylor & Francis, Ltd.]","source":"JSTOR","title":"A Note on the Delta Method","type":"article-journal","URL":"https://www.jstor.org/stable/2684406","volume":"46"},{"id":"ohlssonVariabilityEcologicalRelevance2024","abstract":"Broad-scale interaction patterns among species in food webs can be identified using the group model, which identifies groups of species, sharing similar sets of predators and prey from other groups. These shared relationships are relevant for the functionality of species. The group model originates from stochastic block models, meaning the obtained group structures of the same food web can differ in multiple runs. A single best partition may miss relevant information, and a consensus solution may blur complementary communities. Hence, it is highly relevant to analyze the full solution landscape while searching for the optimal partitioning of species. In particular, a narrow solution landscape would highlight the reliability of the identified groups. Here, using five empirical food webs, we analyze their respective solution landscape based on multiple group model runs of the same network. By analyzing the solution landscapes, we aim to explain the differences between solutions and what they entail, structurally and ecologically. Our results show that the overall general group structures remain intact across different iterations. While some food webs vary more, differences are commonly limited to a smaller number of groups with seemingly similar species roles. Our results suggest that while the stochastic process of the group model can generate alternate solutions for the same food web, these differences generally involve weaker distinctions of species in a small number of groups rather than a large structural turnover.","accessed":{"date-parts":[["2024",10,31]]},"author":[{"family":"Ohlsson","given":"Mikael"},{"family":"Eklöf","given":"Anna"}],"citation-key":"ohlssonVariabilityEcologicalRelevance2024","container-title":"Ecological Informatics","container-title-short":"Ecological Informatics","DOI":"10.1016/j.ecoinf.2024.102696","ISSN":"15749541","issued":{"date-parts":[["2024",9]]},"language":"en","page":"102696","source":"DOI.org (Crossref)","title":"Variability and ecological relevance of alternative group structures in food webs","type":"article-journal","URL":"https://linkinghub.elsevier.com/retrieve/pii/S1574954124002383","volume":"82"},{"id":"openaiDota2Large2019","abstract":"On April 13th, 2019, OpenAI Five became the first AI system to defeat the world champions at an esports game. The game of Dota 2 presents novel challenges for AI systems such as long time horizons, imperfect information, and complex, continuous state-action spaces, all challenges which will become increasingly central to more capable AI systems. OpenAI Five leveraged existing reinforcement learning techniques, scaled to learn from batches of approximately 2 million frames every 2 seconds. We developed a distributed training system and tools for continual training which allowed us to train OpenAI Five for 10 months. By defeating the Dota 2 world champion (Team OG), OpenAI Five demonstrates that self-play reinforcement learning can achieve superhuman performance on a difficult task.","accessed":{"date-parts":[["2024",2,5]]},"author":[{"family":"OpenAI","given":""},{"family":"Berner","given":"Christopher"},{"family":"Brockman","given":"Greg"},{"family":"Chan","given":"Brooke"},{"family":"Cheung","given":"Vicki"},{"family":"Dębiak","given":"Przemysław"},{"family":"Dennison","given":"Christy"},{"family":"Farhi","given":"David"},{"family":"Fischer","given":"Quirin"},{"family":"Hashme","given":"Shariq"},{"family":"Hesse","given":"Chris"},{"family":"Józefowicz","given":"Rafal"},{"family":"Gray","given":"Scott"},{"family":"Olsson","given":"Catherine"},{"family":"Pachocki","given":"Jakub"},{"family":"Petrov","given":"Michael"},{"family":"Pinto","given":"Henrique P. d O."},{"family":"Raiman","given":"Jonathan"},{"family":"Salimans","given":"Tim"},{"family":"Schlatter","given":"Jeremy"},{"family":"Schneider","given":"Jonas"},{"family":"Sidor","given":"Szymon"},{"family":"Sutskever","given":"Ilya"},{"family":"Tang","given":"Jie"},{"family":"Wolski","given":"Filip"},{"family":"Zhang","given":"Susan"}],"citation-key":"openaiDota2Large2019","DOI":"10.48550/arXiv.1912.06680","issued":{"date-parts":[["2019",12,13]]},"number":"arXiv:1912.06680","publisher":"arXiv","source":"arXiv.org","title":"Dota 2 with Large Scale Deep Reinforcement Learning","type":"article","URL":"http://arxiv.org/abs/1912.06680"},{"id":"ottawafield-naturalistsclubCanadianFieldnaturalist1976","author":[{"family":"Ottawa Field-Naturalists' Club","given":""},{"family":"Club","given":"Ottawa Field-Naturalists'"}],"citation-key":"ottawafield-naturalistsclubCanadianFieldnaturalist1976","ISSN":"0008-3550","issued":{"date-parts":[["1976"]]},"number-of-pages":"568","page":"1-568","publisher":"Ottawa Field-Naturalists' Club","publisher-place":"Ottawa","title":"The Canadian field-naturalist","type":"book","URL":"https://www.biodiversitylibrary.org/item/89149","volume":"90"},{"id":"PartitioningMedoidsProgram1990","abstract":"The prelims comprise: Short Description of the Method How to Use the Program PAM Examples More on the Algorithm and the Program Related Methods and References","accessed":{"date-parts":[["2024",9,13]]},"citation-key":"PartitioningMedoidsProgram1990","container-title":"Finding Groups in Data","DOI":"10.1002/9780470316801.ch2","ISBN":"978-0-470-31680-1","issued":{"date-parts":[["1990"]]},"language":"en","page":"68–125","publisher":"John Wiley & Sons, Ltd","section":"2","source":"Wiley Online Library","title":"Partitioning Around Medoids (Program PAM)","type":"chapter","URL":"https://onlinelibrary.wiley.com/doi/abs/10.1002/9780470316801.ch2"},{"id":"pattersonRecoveryInterblockInformation1971","abstract":"A method is proposed for estimating intra-block and inter-blook weights in the analysis of incomplete block designs with block sizes not necessarily equal. The method consists of maximizing the likelihood, not of all the data, but of a set of selected error contrasts. When block sizes are equal results are identical with those obtained by the method of Nelder (1968) for generally balanced designs. Although mainly concerned with incomplete block designs the paper also gives in outline an extension of the modified maximum likelihood procedure to designs with a more complicated block structure.","accessed":{"date-parts":[["2024",3,17]]},"author":[{"family":"PATTERSON","given":"H. D."},{"family":"THOMPSON","given":"R."}],"citation-key":"pattersonRecoveryInterblockInformation1971","container-title":"Biometrika","container-title-short":"Biometrika","DOI":"10.1093/biomet/58.3.545","ISSN":"0006-3444","issue":"3","issued":{"date-parts":[["1971",12,1]]},"page":"545–554","source":"Silverchair","title":"Recovery of inter-block information when block sizes are unequal","type":"article-journal","URL":"https://doi.org/10.1093/biomet/58.3.545","volume":"58"},{"id":"pavlopoulosBipartiteGraphsSystems2018","abstract":"The latest advances in high-throughput techniques during the past decade allowed the systems biology field to expand significantly. Today, the focus of biologists has shifted from the study of individual biological components to the study of complex biological systems and their dynamics at a larger scale. Through the discovery of novel bioentity relationships, researchers reveal new information about biological functions and processes. Graphs are widely used to represent bioentities such as proteins, genes, small molecules, ligands, and others such as nodes and their connections as edges within a network. In this review, special focus is given to the usability of bipartite graphs and their impact on the field of network biology and medicine. Furthermore, their topological properties and how these can be applied to certain biological case studies are discussed. Finally, available methodologies and software are presented, and useful insights on how bipartite graphs can shape the path toward the solution of challenging biological problems are provided.","accessed":{"date-parts":[["2023",6,15]]},"author":[{"family":"Pavlopoulos","given":"Georgios A"},{"family":"Kontou","given":"Panagiota I"},{"family":"Pavlopoulou","given":"Athanasia"},{"family":"Bouyioukos","given":"Costas"},{"family":"Markou","given":"Evripides"},{"family":"Bagos","given":"Pantelis G"}],"citation-key":"pavlopoulosBipartiteGraphsSystems2018","container-title":"GigaScience","container-title-short":"GigaScience","DOI":"10.1093/gigascience/giy014","ISSN":"2047-217X","issue":"4","issued":{"date-parts":[["2018",4,1]]},"page":"giy014","source":"Silverchair","title":"Bipartite graphs in systems biology and medicine: a survey of methods and applications","title-short":"Bipartite graphs in systems biology and medicine","type":"article-journal","URL":"https://doi.org/10.1093/gigascience/giy014","volume":"7"},{"id":"pavlopoulosBipartiteGraphsSystems2018","abstract":"The latest advances in high-throughput techniques during the past decade allowed the systems biology field to expand significantly. Today, the focus of biologists has shifted from the study of individual biological components to the study of complex biological systems and their dynamics at a larger scale. Through the discovery of novel bioentity relationships, researchers reveal new information about biological functions and processes. Graphs are widely used to represent bioentities such as proteins, genes, small molecules, ligands, and others such as nodes and their connections as edges within a network. In this review, special focus is given to the usability of bipartite graphs and their impact on the field of network biology and medicine. Furthermore, their topological properties and how these can be applied to certain biological case studies are discussed. Finally, available methodologies and software are presented, and useful insights on how bipartite graphs can shape the path toward the solution of challenging biological problems are provided.","accessed":{"date-parts":[["2023",6,15]]},"author":[{"family":"Pavlopoulos","given":"Georgios A"},{"family":"Kontou","given":"Panagiota I"},{"family":"Pavlopoulou","given":"Athanasia"},{"family":"Bouyioukos","given":"Costas"},{"family":"Markou","given":"Evripides"},{"family":"Bagos","given":"Pantelis G"}],"citation-key":"pavlopoulosBipartiteGraphsSystems2018","container-title":"GigaScience","DOI":"10.1093/gigascience/giy014","ISSN":"2047-217X","issue":"4","issued":{"date-parts":[["2018",4,1]]},"page":"giy014","title":"Bipartite graphs in systems biology and medicine: a survey of methods and applications","title-short":"Bipartite graphs in systems biology and medicine","type":"article-journal","URL":"https://doi.org/10.1093/gigascience/giy014","volume":"7"},{"id":"pavlopoulosBipartiteGraphsSystems2018a","abstract":"The latest advances in high-throughput techniques during the past decade allowed the systems biology field to expand significantly. Today, the focus of biologists has shifted from the study of individual biological components to the study of complex biological systems and their dynamics at a larger scale. Through the discovery of novel bioentity relationships, researchers reveal new information about biological functions and processes. Graphs are widely used to represent bioentities such as proteins, genes, small molecules, ligands, and others such as nodes and their connections as edges within a network. In this review, special focus is given to the usability of bipartite graphs and their impact on the field of network biology and medicine. Furthermore, their topological properties and how these can be applied to certain biological case studies are discussed. Finally, available methodologies and software are presented, and useful insights on how bipartite graphs can shape the path toward the solution of challenging biological problems are provided.","accessed":{"date-parts":[["2025",4,10]]},"author":[{"family":"Pavlopoulos","given":"Georgios A"},{"family":"Kontou","given":"Panagiota I"},{"family":"Pavlopoulou","given":"Athanasia"},{"family":"Bouyioukos","given":"Costas"},{"family":"Markou","given":"Evripides"},{"family":"Bagos","given":"Pantelis G"}],"citation-key":"pavlopoulosBipartiteGraphsSystems2018a","container-title":"GigaScience","DOI":"10.1093/gigascience/giy014","ISSN":"2047-217X","issue":"4","issued":{"date-parts":[["2018",4,1]]},"language":"en","license":"http://creativecommons.org/licenses/by/4.0/","page":"giy014","source":"DOI.org (Crossref)","title":"Bipartite graphs in systems biology and medicine: a survey of methods and applications","title-short":"Bipartite graphs in systems biology and medicine","type":"article-journal","URL":"https://academic.oup.com/gigascience/article/doi/10.1093/gigascience/giy014/4875933","volume":"7"},{"id":"pavlovicMultisubjectStochasticBlockmodels2020","abstract":"There is considerable interest in elucidating the cluster structure of brain networks in terms of modules, blocks or clusters of similar nodes. However, it is currently challenging to handle data on multiple subjects since most of the existing methods are applicable only on a subject-by-subject basis or for analysis of an average group network. The main limitation of per-subject models is that there is no obvious way to combine the results for group comparisons, and of group-averaged models that they do not reflect the variability between subjects. Here, we propose two new extensions of the classical Stochastic Blockmodel (SBM) that use a mixture model to estimate blocks or clusters of connected nodes, combined with a regression model to capture the effects of subject-level covariates on individual differences in cluster structure. The proposed Multi-Subject Stochastic Blockmodels (MS-SBMs) can flexibly account for between-subject variability in terms of homogeneous or heterogeneous covariate effects on connectivity using subject demographics such as age or diagnostic status. Using synthetic data, representing a range of block sizes and cluster structures, we investigate the accuracy of the estimated MS-SBM parameters as well as the validity of inference procedures based on the Wald, likelihood ratio and permutation tests. We show that the proposed multi-subject SBMs recover the true cluster structure of synthetic networks more accurately and adaptively than standard methods for modular decomposition (i.e. the Fast Louvain and Newman Spectral algorithms). Permutation tests of MS-SBM parameters were more robustly valid for statistical inference and Type I error control than tests based on standard asymptotic assumptions. Applied to analysis of multi-subject resting-state fMRI networks (13 healthy volunteers; 12 people with schizophrenia; n=268 brain regions), we show that Heterogeneous Stochastic Blockmodel (Het-SBM) identifies a range of network topologies simultaneously, including modular and core structures.","author":[{"family":"Pavlović","given":"Dragana M."},{"family":"Guillaume","given":"Bryan R. L."},{"family":"Towlson","given":"Emma K."},{"family":"Kuek","given":"Nicole M. Y."},{"family":"Afyouni","given":"Soroosh"},{"family":"Vértes","given":"Petra E."},{"family":"Yeo","given":"B. T. Thomas"},{"family":"Bullmore","given":"Edward T."},{"family":"Nichols","given":"Thomas E."}],"citation-key":"pavlovicMultisubjectStochasticBlockmodels2020","container-title":"NeuroImage","container-title-short":"Neuroimage","DOI":"10.1016/j.neuroimage.2020.116611","ISSN":"1095-9572","issued":{"date-parts":[["2020",10,15]]},"language":"eng","note":"Read_Status: New\nRead_Status_Date: 2025-09-19T14:04:30.797Z","page":"116611","PMID":"32058004","source":"PubMed","title":"Multi-subject Stochastic Blockmodels for adaptive analysis of individual differences in human brain network cluster structure","type":"article-journal","volume":"220"},{"id":"payneFiniteMixturesMultivariate2023","abstract":"A mixture of multivariate Poisson-log normal factor analyzers is introduced by imposing constraints on the covariance matrix, which resulted in flexible models for clustering purposes. In particular, a class of eight parsimonious mixture models based on the mixtures of factor analyzers model are introduced. Variational Gaussian approximation is used for parameter estimation, and information criteria are used for model selection. The proposed models are explored in the context of clustering discrete data arising from RNA sequencing studies. Using real and simulated data, the models are shown to give favourable clustering performance. The GitHub R package for this work is available at https://github.com/anjalisilva/mixMPLNFA and is released under the open-source MIT license.","accessed":{"date-parts":[["2025",7,2]]},"author":[{"family":"Payne","given":"Andrea"},{"family":"Silva","given":"Anjali"},{"family":"Rothstein","given":"Steven J."},{"family":"McNicholas","given":"Paul D."},{"family":"Subedi","given":"Sanjeena"}],"citation-key":"payneFiniteMixturesMultivariate2023","DOI":"10.48550/arXiv.2311.07762","issued":{"date-parts":[["2023",11,13]]},"note":"Read_Status: New\nRead_Status_Date: 2025-07-02T09:31:47.579Z","number":"arXiv:2311.07762","publisher":"arXiv","source":"arXiv.org","title":"Finite Mixtures of Multivariate Poisson-Log Normal Factor Analyzers for Clustering Count Data","type":"article","URL":"http://arxiv.org/abs/2311.07762"},{"id":"peixotoBayesianStochasticBlockmodeling2023","abstract":"This chapter provides a self-contained introduction to the use of Bayesian inference to extract large-scale modular structures from network data, based on the stochastic blockmodel (SBM), as well as its degree-corrected and overlapping generalizations. We focus on nonparametric formulations that allow their inference in a manner that prevents overfitting, and enables model selection. We discuss aspects of the choice of priors, in particular how to avoid underfitting via increased Bayesian hierarchies, and we contrast the task of sampling network partitions from the posterior distribution with finding the single point estimate that maximizes it, while describing efficient algorithms to perform either one. We also show how inferring the SBM can be used to predict missing and spurious links, and shed light on the fundamental limitations of the detectability of modular structures in networks.","accessed":{"date-parts":[["2025",1,26]]},"author":[{"family":"Peixoto","given":"Tiago P."}],"citation-key":"peixotoBayesianStochasticBlockmodeling2023","DOI":"10.1002/9781119483298.ch11","issued":{"date-parts":[["2023",3,22]]},"language":"en","source":"arXiv.org","title":"Bayesian stochastic blockmodeling","type":"article","URL":"http://arxiv.org/abs/1705.10225"},{"id":"peixotoEfficientMonteCarlo2014","abstract":"We present an efficient algorithm for the inference of stochastic block models in large networks. The algorithm can be used as an optimized Markov chain Monte Carlo (MCMC) method, with a fast mixing time and a much reduced susceptibility to getting trapped in metastable states, or as a greedy agglomerative heuristic, with an almost linear 𝑂(𝑁ln2𝑁) complexity, where 𝑁 is the number of nodes in the network, independent of the number of blocks being inferred. We show that the heuristic is capable of delivering results which are indistinguishable from the more exact and numerically expensive MCMC method in many artificial and empirical networks, despite being much faster. The method is entirely unbiased towards any specific mixing pattern, and in particular it does not favor assortative community structures.","accessed":{"date-parts":[["2025",1,26]]},"author":[{"family":"Peixoto","given":"Tiago P."}],"citation-key":"peixotoEfficientMonteCarlo2014","container-title":"Physical Review E","container-title-short":"Phys. Rev. E","DOI":"10.1103/PhysRevE.89.012804","issue":"1","issued":{"date-parts":[["2014",1,13]]},"page":"012804","publisher":"American Physical Society","source":"APS","title":"Efficient Monte Carlo and greedy heuristic for the inference of stochastic block models","type":"article-journal","URL":"https://link.aps.org/doi/10.1103/PhysRevE.89.012804","volume":"89"},{"id":"peixotoHierarchicalBlockStructures2014","accessed":{"date-parts":[["2025",9,26]]},"author":[{"family":"Peixoto","given":"Tiago P."}],"citation-key":"peixotoHierarchicalBlockStructures2014","container-title":"Physical Review X","container-title-short":"Phys. Rev. X","DOI":"10.1103/PhysRevX.4.011047","ISSN":"2160-3308","issue":"1","issued":{"date-parts":[["2014",3,24]]},"language":"en","license":"http://creativecommons.org/licenses/by/3.0/","note":"Read_Status: New\nRead_Status_Date: 2025-09-26T08:27:38.586Z","page":"011047","source":"DOI.org (Crossref)","title":"Hierarchical Block Structures and High-Resolution Model Selection in Large Networks","type":"article-journal","URL":"https://link.aps.org/doi/10.1103/PhysRevX.4.011047","volume":"4"},{"id":"perkinsDDBasicRules2018","author":[{"family":"Perkins","given":"Christopher"},{"family":"Lee","given":"Peter"},{"family":"Townshend","given":"Steve"},{"family":"Cordell","given":"Bruce R"},{"family":"Mohan","given":"Kim"}],"citation-key":"perkinsDDBasicRules2018","issued":{"date-parts":[["2018",11]]},"language":"en","source":"Zotero","title":"D&D Basic Rules, Version 1.0, Released November 2018","type":"book"},{"id":"perrot-dockesIntroductionMultiVarSel2019","author":[{"family":"Perrot-Dockès","given":"Marie"},{"family":"Lévy-Leduc","given":"Céline"},{"family":"Chiquet","given":"Julien"}],"citation-key":"perrot-dockesIntroductionMultiVarSel2019","issued":{"date-parts":[["2019",3,21]]},"language":"en","source":"Zotero","title":"Introduction to MultiVarSel","type":"article-journal"},{"id":"petersenHttpMatrixcookbookcom","author":[{"family":"Petersen","given":"Kaare Brandt"},{"family":"Pedersen","given":"Michael Syskind"}],"citation-key":"petersenHttpMatrixcookbookcom","language":"en","source":"Zotero","title":"[ http://matrixcookbook.com ]","type":"article-journal"},{"id":"petersenMatrixCookbook2012","author":[{"family":"Petersen","given":"Kaare Brandt"},{"family":"Pedersen","given":"Michael Syskind"}],"citation-key":"petersenMatrixCookbook2012","edition":"Version 20121115","issued":{"date-parts":[["2012"]]},"language":"en","source":"Zotero","title":"The Matrix Cookbook","type":"book","URL":"http://matrixcookbook.com"},{"id":"peyreComputationalOptimalTransport2020","abstract":"Optimal transport (OT) theory can be informally described using the words of the French mathematician Gaspard Monge (1746-1818): A worker with a shovel in hand has to move a large pile of sand lying on a construction site. The goal of the worker is to erect with all that sand a target pile with a prescribed shape (for example, that of a giant sand castle). Naturally, the worker wishes to minimize her total effort, quantified for instance as the total distance or time spent carrying shovelfuls of sand. Mathematicians interested in OT cast that problem as that of comparing two probability distributions, two different piles of sand of the same volume. They consider all of the many possible ways to morph, transport or reshape the first pile into the second, and associate a \"global\" cost to every such transport, using the \"local\" consideration of how much it costs to move a grain of sand from one place to another. Recent years have witnessed the spread of OT in several fields, thanks to the emergence of approximate solvers that can scale to sizes and dimensions that are relevant to data sciences. Thanks to this newfound scalability, OT is being increasingly used to unlock various problems in imaging sciences (such as color or texture processing), computer vision and graphics (for shape manipulation) or machine learning (for regression, classification and density fitting). This short book reviews OT with a bias toward numerical methods and their applications in data sciences, and sheds lights on the theoretical properties of OT that make it particularly useful for some of these applications.","accessed":{"date-parts":[["2024",5,14]]},"author":[{"family":"Peyré","given":"Gabriel"},{"family":"Cuturi","given":"Marco"}],"citation-key":"peyreComputationalOptimalTransport2020","DOI":"10.48550/arXiv.1803.00567","issued":{"date-parts":[["2020",3,18]]},"number":"arXiv:1803.00567","publisher":"arXiv","source":"arXiv.org","title":"Computational Optimal Transport","type":"article","URL":"http://arxiv.org/abs/1803.00567"},{"id":"peyreGromovWassersteinAveragingKernel","abstract":"This paper presents a new technique for computing the barycenter of a set of distance or kernel matrices. These matrices, which define the interrelationships between points sampled from individual domains, are not required to have the same size or to be in row-by-row correspondence. We compare these matrices using the softassign criterion, which measures the minimum distortion induced by a probabilistic map from the rows of one similarity matrix to the rows of another; this criterion amounts to a regularized version of the Gromov-Wasserstein (GW) distance between metric-measure spaces. The barycenter is then defined as a Fr´echet mean of the input matrices with respect to this criterion, minimizing a weighted sum of softassign values. We provide a fast iterative algorithm for the resulting nonconvex optimization problem, built upon state-ofthe-art tools for regularized optimal transportation. We demonstrate its application to the computation of shape barycenters and to the prediction of energy levels from molecular configurations in quantum chemistry.","author":[{"family":"Peyré","given":"Gabriel"},{"family":"Cuturi","given":"Marco"},{"family":"Solomon","given":"Justin"}],"citation-key":"peyreGromovWassersteinAveragingKernel","language":"en","note":"Read_Status: New\nRead_Status_Date: 2025-06-11T16:01:10.274Z","source":"Zotero","title":"Gromov-Wasserstein Averaging of Kernel and Distance Matrices","type":"article-journal"},{"id":"pichonTellingMutualisticAntagonistic","abstract":"Characterizing and understanding the processes that shape the structure of ecological networks, which represent who interacts with whom in a community, has many implications in ecology, evolutionary biology and conservation. A highly debated question is whether and how the structure of a bipartite ecological network differs between antagonistic (e.g. herbivory) and mutualistic (e.g. pollination) interaction types. Here, we tackle this question by using a multiscale characterization of network structure, machine learning tools, and a large database of empirical and simulated bipartite networks. Contrary to previous studies focusing on global structural metrics, such as nestedness and modularity, which concluded that antagonistic and mutualistic networks cannot be told apart from only their structure, we find that they can be told apart by combining a meso-scale characterization of their structure and supervised machine learning. Motif frequencies appear particularly informative, with an over-representation of densely connected motifs in antagonistic networks and of motifs with asymmetrical specialization in mutualistic networks. These structural properties can be used to predict the type of interaction with relatively good confidence. Beyond this classical mutualism/antagonism dichotomy, we also find significant structural uniqueness linked to specific ecologies (e.g. pollination, parasitism). Our results clarify structural differences between antagonistic and mutualistic networks and suggest the investigation of the structural uniqueness of specific ecologies as a promising approach for characterizing interactions beyond the coarse antagonistic/mutualistic dichotomy.","accessed":{"date-parts":[["2024",5,27]]},"author":[{"family":"Pichon","given":"Benoît"},{"family":"Le Goff","given":"Rémy"},{"family":"Morlon","given":"Hélène"},{"family":"Perez-Lamarque","given":"Benoît"}],"citation-key":"pichonTellingMutualisticAntagonistic","container-title":"Methods in Ecology and Evolution","DOI":"10.1111/2041-210X.14328","ISSN":"2041-210X","issue":"n/a","language":"en","license":"© 2024 The Authors. Methods in Ecology and Evolution published by John Wiley & Sons Ltd on behalf of British Ecological Society.","source":"Wiley Online Library","title":"Telling mutualistic and antagonistic ecological networks apart by learning their multiscale structure","type":"article-journal","URL":"https://onlinelibrary.wiley.com/doi/abs/10.1111/2041-210X.14328","volume":"n/a"},{"id":"pichonTellingMutualisticAntagonistic2024","abstract":"Characterizing and understanding the processes that shape the structure of ecological networks, which represent who interacts with whom in a community, has many implications in ecology, evolutionary biology and conservation. A highly debated question is whether and how the structure of a bipartite ecological network differs between antagonistic (e.g. herbivory) and mutualistic (e.g. pollination) interaction types. Here, we tackle this question by using a multiscale characterization of network structure, machine learning tools, and a large database of empirical and simulated bipartite networks. Contrary to previous studies focusing on global structural metrics, such as nestedness and modularity, which concluded that antagonistic and mutualistic networks cannot be told apart from only their structure, we find that they can be told apart by combining a meso-scale characterization of their structure and supervised machine learning. Motif frequencies appear particularly informative, with an over-representation of densely connected motifs in antagonistic networks and of motifs with asymmetrical specialization in mutualistic networks. These structural properties can be used to predict the type of interaction with relatively good confidence. Beyond this classical mutualism/antagonism dichotomy, we also find significant structural uniqueness linked to specific ecologies (e.g. pollination, parasitism). Our results clarify structural differences between antagonistic and mutualistic networks and suggest the investigation of the structural uniqueness of specific ecologies as a promising approach for characterizing interactions beyond the coarse antagonistic/mutualistic dichotomy.","accessed":{"date-parts":[["2024",6,17]]},"author":[{"family":"Pichon","given":"Benoît"},{"family":"Le Goff","given":"Rémy"},{"family":"Morlon","given":"Hélène"},{"family":"Perez-Lamarque","given":"Benoît"}],"citation-key":"pichonTellingMutualisticAntagonistic2024","container-title":"Methods in Ecology and Evolution","DOI":"10.1111/2041-210X.14328","ISSN":"2041-210X","issue":"6","issued":{"date-parts":[["2024"]]},"language":"en","page":"1113–1128","source":"Wiley Online Library","title":"Telling mutualistic and antagonistic ecological networks apart by learning their multiscale structure","type":"article-journal","URL":"https://onlinelibrary.wiley.com/doi/abs/10.1111/2041-210X.14328","volume":"15"},{"id":"pichonTellingMutualisticAntagonistic2024","abstract":"Characterizing and understanding the processes that shape the structure of ecological networks, which represent who interacts with whom in a community, has many implications in ecology, evolutionary biology and conservation. A highly debated question is whether and how the structure of a bipartite ecological network differs between antagonistic (e.g. herbivory) and mutualistic (e.g. pollination) interaction types. Here, we tackle this question by using a multiscale characterization of network structure, machine learning tools, and a large database of empirical and simulated bipartite networks. Contrary to previous studies focusing on global structural metrics, such as nestedness and modularity, which concluded that antagonistic and mutualistic networks cannot be told apart from only their structure, we find that they can be told apart by combining a meso-scale characterization of their structure and supervised machine learning. Motif frequencies appear particularly informative, with an over-representation of densely connected motifs in antagonistic networks and of motifs with asymmetrical specialization in mutualistic networks. These structural properties can be used to predict the type of interaction with relatively good confidence. Beyond this classical mutualism/antagonism dichotomy, we also find significant structural uniqueness linked to specific ecologies (e.g. pollination, parasitism). Our results clarify structural differences between antagonistic and mutualistic networks and suggest the investigation of the structural uniqueness of specific ecologies as a promising approach for characterizing interactions beyond the coarse antagonistic/mutualistic dichotomy.","accessed":{"date-parts":[["2024",6,17]]},"author":[{"family":"Pichon","given":"Benoît"},{"family":"Le Goff","given":"Rémy"},{"family":"Morlon","given":"Hélène"},{"family":"Perez-Lamarque","given":"Benoît"}],"citation-key":"pichonTellingMutualisticAntagonistic2024","container-title":"Methods in Ecology and Evolution","DOI":"10.1111/2041-210X.14328","ISSN":"2041-210X","issue":"6","issued":{"date-parts":[["2024"]]},"language":"english","page":"1113–1128","title":"Telling mutualistic and antagonistic ecological networks apart by learning their multiscale structure","type":"article-journal","URL":"https://onlinelibrary.wiley.com/doi/abs/10.1111/2041-210X.14328","volume":"15"},{"id":"priamNegativeBinomialLatent2024","abstract":"Constrained latent block models (LBM) are proposed for contingency matrices herein. Several discrete distributions related to the usual Poisson one are compared for modeling the blocks in a co-clustering and a reduction of the rows and columns.","accessed":{"date-parts":[["2025",10,29]]},"author":[{"family":"Priam","given":"Rodolphe"}],"citation-key":"priamNegativeBinomialLatent2024","issued":{"date-parts":[["2024",11]]},"note":"Read_Status: New\nRead_Status_Date: 2025-10-29T15:37:28.803Z","source":"HAL","title":"Negative binomial latent block model with generalized constraints","type":"manuscript","URL":"https://hal.science/hal-03172789"},{"id":"pudloReliableABCModel2016","abstract":"Motivation: Approximate Bayesian computation (ABC) methods provide an elaborate approach to Bayesian inference on complex models, including model choice. Both theoretical arguments and simulation experiments indicate, however, that model posterior probabilities may be poorly evaluated by standard ABC techniques.Results: We propose a novel approach based on a machine learning tool named random forests (RF) to conduct selection among the highly complex models covered by ABC algorithms. We thus modify the way Bayesian model selection is both understood and operated, in that we rephrase the inferential goal as a classification problem, first predicting the model that best fits the data with RF and postponing the approximation of the posterior probability of the selected model for a second stage also relying on RF. Compared with earlier implementations of ABC model choice, the ABC RF approach offers several potential improvements: (i) it often has a larger discriminative power among the competing models, (ii) it is more robust against the number and choice of statistics summarizing the data, (iii) the computing effort is drastically reduced (with a gain in computation efficiency of at least 50) and (iv) it includes an approximation of the posterior probability of the selected model. The call to RF will undoubtedly extend the range of size of datasets and complexity of models that ABC can handle. We illustrate the power of this novel methodology by analyzing controlled experiments as well as genuine population genetics datasets.Availability and implementation: The proposed methodology is implemented in the R package abcrf available on the CRAN.Contact: jean-michel.marin@umontpellier.frSupplementary information: Supplementary data are available at Bioinformatics online.","accessed":{"date-parts":[["2026",4,7]]},"author":[{"family":"Pudlo","given":"Pierre"},{"family":"Marin","given":"Jean-Michel"},{"family":"Estoup","given":"Arnaud"},{"family":"Cornuet","given":"Jean-Marie"},{"family":"Gautier","given":"Mathieu"},{"family":"Robert","given":"Christian P."}],"citation-key":"pudloReliableABCModel2016","container-title":"Bioinformatics","container-title-short":"Bioinformatics","DOI":"10.1093/bioinformatics/btv684","ISSN":"1367-4803","issue":"6","issued":{"date-parts":[["2016",3,15]]},"note":"Read_Status: New\nRead_Status_Date: 2026-04-07T13:23:20.640Z","page":"859–866","source":"Silverchair","title":"Reliable ABC model choice via random forests","type":"article-journal","URL":"https://doi.org/10.1093/bioinformatics/btv684","volume":"32"},{"id":"puTreeEnhancedLatentSpace2025","abstract":"Latent space models have garnered significant attention in the analysis of two-mode networks. In numerous applications, auxiliary information in the form of a hierarchical tree structure, which elucidates the interrelationships between nodes and provides extensive insights into connectivity patterns, can be easily obtained. To harness the potential of such tree-structured information, we introduce an innovative tree-enhanced latent space model (TLSM) for two-mode networks. In this framework, each node is characterized by a latent embedding vector, reparameterized as the aggregate of intermediate vectors corresponding to nodes within the tree structure. By optimizing the log-likelihood function augmented with a tree-based regularization term, the proposed model facilitates the simultaneous estimation of embedding vectors and the derivation of interpretable community structures. We have developed an efficient Alternating Direction Method of Multipliers (ADMM) algorithm to solve the resulting optimization problem. Theoretical analysis establishes the consistency of the proposed estimator under some mild conditions. Furthermore, comprehensive simulation studies and empirical applications on the Amazon review dataset substantiate the efficacy and practical relevance of the proposed model. Supplementary materials for this article are available online.","accessed":{"date-parts":[["2025",9,23]]},"author":[{"family":"Pu","given":"Dan"},{"family":"Fan","given":"Xinyan"},{"family":"Fang","given":"Kuangnan"}],"citation-key":"puTreeEnhancedLatentSpace2025","container-title":"Journal of Computational and Graphical Statistics","DOI":"10.1080/10618600.2025.2527295","ISSN":"1061-8600","issue":"0","issued":{"date-parts":[["2025",6,21]]},"note":"Read_Status: Read\nRead_Status_Date: 2025-09-24T13:31:51.261Z","page":"1–9","publisher":"ASA Website","source":"Taylor and Francis+NEJM","title":"Tree-Enhanced Latent Space Models for Two-Mode Networks","type":"article-journal","URL":"https://doi.org/10.1080/10618600.2025.2527295","volume":"0"},{"id":"ramos-jilibertoTopologicalChangeAndean2010","abstract":"Pollination interaction networks exhibit structural regularities across a wide range of natural environments. Long-tailed degree distribution, nestedness, and modularity are the most prevalent topological patterns found in most bipartite networks analyzed up to day. In this work we evaluate the variation of these topological properties along an altitudinal gradient. To this end, we examined four plant–pollinator networks from the Chilean Andes at 33°S, in range from 1800 to 3600m elevation. Our results indicate that network topology is strongly and systematically affected by elevation. At increasing altitude, the number of potential visitors per plant decreased, and species’ degree distributions are closer to random expectations. On the other hand, the nested structure of mutualistic interactions systematically decreased with elevation, and network modularity was significantly higher than random expectations over the entire altitudinal range. In addition, at increasing elevations the pollination networks were organized in fewer and more strongly connected modules. Our results suggest that the severe abiotic conditions found at increased elevations translate into less organized pollination networks.","accessed":{"date-parts":[["2023",6,15]]},"author":[{"family":"Ramos-Jiliberto","given":"Rodrigo"},{"family":"Domínguez","given":"Daniela"},{"family":"Espinoza","given":"Claudia"},{"family":"López","given":"Gioconda"},{"family":"Valdovinos","given":"Fernanda S."},{"family":"Bustamante","given":"Ramiro O."},{"family":"Medel","given":"Rodrigo"}],"citation-key":"ramos-jilibertoTopologicalChangeAndean2010","container-title":"Ecological Complexity","container-title-short":"Ecological Complexity","DOI":"10.1016/j.ecocom.2009.06.001","ISSN":"1476-945X","issue":"1","issued":{"date-parts":[["2010",3,1]]},"language":"en","page":"86–90","source":"ScienceDirect","title":"Topological change of Andean plant–pollinator networks along an altitudinal gradient","type":"article-journal","URL":"https://www.sciencedirect.com/science/article/pii/S1476945X09000622","volume":"7"},{"id":"ratnaInclusiveAnalysisPerformance2025","accessed":{"date-parts":[["2025",1,15]]},"author":[{"family":"Ratna","given":"S."},{"family":"Singh","given":"Sukhdeep"},{"family":"Sharma","given":"Anuj"}],"citation-key":"ratnaInclusiveAnalysisPerformance2025","container-title":"Computer Science Review","container-title-short":"Computer Science Review","DOI":"10.1016/j.cosrev.2024.100722","ISSN":"15740137","issued":{"date-parts":[["2025",5]]},"language":"en","page":"100722","source":"DOI.org (Crossref)","title":"An inclusive analysis for performance and efficiency of graph neural network models for node classification","type":"article-journal","URL":"https://linkinghub.elsevier.com/retrieve/pii/S1574013724001059","volume":"56"},{"id":"raynalABCRandomForests2019","abstract":"Approximate Bayesian computation (ABC) has grown into a standard methodology that manages Bayesian inference for models associated with intractable likelihood functions. Most ABC implementations require the preliminary selection of a vector of informative statistics summarizing raw data. Furthermore, in almost all existing implementations, the tolerance level that separates acceptance from rejection of simulated parameter values needs to be calibrated.We propose to conduct likelihood-free Bayesian inferences about parameters with no prior selection of the relevant components of the summary statistics and bypassing the derivation of the associated tolerance level. The approach relies on the random forest (RF) methodology of Breiman (2001) applied in a (non-parametric) regression setting. We advocate the derivation of a new RF for each component of the parameter vector of interest. When compared with earlier ABC solutions, this method offers significant gains in terms of robustness to the choice of the summary statistics, does not depend on any type of tolerance level, and is a good trade-off in term of quality of point estimator precision and credible interval estimations for a given computing time. We illustrate the performance of our methodological proposal and compare it with earlier ABC methods on a Normal toy example and a population genetics example dealing with human population evolution.All methods designed here have been incorporated in the R package abcrf (version 1.7.1) available on CRAN.Supplementary data are available at Bioinformatics online.","accessed":{"date-parts":[["2026",4,7]]},"author":[{"family":"Raynal","given":"Louis"},{"family":"Marin","given":"Jean-Michel"},{"family":"Pudlo","given":"Pierre"},{"family":"Ribatet","given":"Mathieu"},{"family":"Robert","given":"Christian P"},{"family":"Estoup","given":"Arnaud"}],"citation-key":"raynalABCRandomForests2019","container-title":"Bioinformatics","container-title-short":"Bioinformatics","DOI":"10.1093/bioinformatics/bty867","ISSN":"1367-4803","issue":"10","issued":{"date-parts":[["2019",5,15]]},"note":"Read_Status: New\nRead_Status_Date: 2026-04-07T13:22:59.866Z","page":"1720–1728","source":"Silverchair","title":"ABC random forests for Bayesian parameter inference","type":"article-journal","URL":"https://doi.org/10.1093/bioinformatics/bty867","volume":"35"},{"id":"rebafkaModelbasedClusteringMultiple2023","abstract":"The paper tackles the problem of clustering multiple networks, directed or not, that do not share the same set of vertices, into groups of networks with similar topology. A statistical model-based approach based on a finite mixture of stochastic block models is proposed. A clustering is obtained by maximizing the integrated classification likelihood criterion. This is done by a hierarchical agglomerative algorithm, that starts from singleton clusters and successively merges clusters of networks. As such, a sequence of nested clusterings is computed that can be represented by a dendrogram providing valuable insights on the collection of networks. Using a Bayesian framework, model selection is performed in an automated way since the algorithm stops when the best number of clusters is attained. The algorithm is computationally efficient, when carefully implemented. The aggregation of clusters requires a means to overcome the label-switching problem of the stochastic block model and to match the block labels of the networks. To address this problem, a new tool is proposed based on a comparison of the graphons of the associated stochastic block models. The clustering approach is assessed on synthetic data. An application to a set of ecological networks illustrates the interpretability of the obtained results.","accessed":{"date-parts":[["2024",7,22]]},"author":[{"family":"Rebafka","given":"Tabea"}],"citation-key":"rebafkaModelbasedClusteringMultiple2023","DOI":"10.48550/arXiv.2211.02314","issued":{"date-parts":[["2023",11,6]]},"number":"arXiv:2211.02314","publisher":"arXiv","source":"arXiv.org","title":"Model-based clustering of multiple networks with a hierarchical algorithm","type":"article","URL":"http://arxiv.org/abs/2211.02314"},{"id":"rebafkaModelbasedClusteringMultiple2023a","abstract":"The paper tackles the problem of clustering multiple networks, directed or not, that do not share the same set of vertices, into groups of networks with similar topology. A statistical model-based approach based on a finite mixture of stochastic block models is proposed. A clustering is obtained by maximizing the integrated classification likelihood criterion. This is done by a hierarchical agglomerative algorithm, that starts from singleton clusters and successively merges clusters of networks. As such, a sequence of nested clusterings is computed that can be represented by a dendrogram providing valuable insights on the collection of networks. Using a Bayesian framework, model selection is performed in an automated way since the algorithm stops when the best number of clusters is attained. The algorithm is computationally efficient, when carefully implemented. The aggregation of clusters requires a means to overcome the label-switching problem of the stochastic block model and to match the block labels of the networks. To address this problem, a new tool is proposed based on a comparison of the graphons of the associated stochastic block models. The clustering approach is assessed on synthetic data. An application to a set of ecological networks illustrates the interpretability of the obtained results.","accessed":{"date-parts":[["2025",12,1]]},"author":[{"family":"Rebafka","given":"Tabea"}],"citation-key":"rebafkaModelbasedClusteringMultiple2023a","container-title":"Statistics and computing","container-title-short":"Stat Comput","DOI":"10.1007/s11222-023-10329-w","ISSN":"1573-1375","issue":"1","issued":{"date-parts":[["2023",11,7]]},"language":"english","page":"32","title":"Model-based clustering of multiple networks with a hierarchical algorithm","type":"article-journal","URL":"https://doi.org/10.1007/s11222-023-10329-w","volume":"34"},{"id":"rebafkaModelbasedClusteringMultiple2023b","abstract":"The paper tackles the problem of clustering multiple networks, directed or not, that do not share the same set of vertices, into groups of networks with similar topology. A statistical model-based approach based on a finite mixture of stochastic block models is proposed. A clustering is obtained by maximizing the integrated classification likelihood criterion. This is done by a hierarchical agglomerative algorithm, that starts from singleton clusters and successively merges clusters of networks. As such, a sequence of nested clusterings is computed that can be represented by a dendrogram providing valuable insights on the collection of networks. Using a Bayesian framework, model selection is performed in an automated way since the algorithm stops when the best number of clusters is attained. The algorithm is computationally efficient, when carefully implemented. The aggregation of clusters requires a means to overcome the label-switching problem of the stochastic block model and to match the block labels of the networks. To address this problem, a new tool is proposed based on a comparison of the graphons of the associated stochastic block models. The clustering approach is assessed on synthetic data. An application to a set of ecological networks illustrates the interpretability of the obtained results.","accessed":{"date-parts":[["2025",12,1]]},"author":[{"family":"Rebafka","given":"Tabea"}],"citation-key":"rebafkaModelbasedClusteringMultiple2023b","container-title":"Statistics and Computing","container-title-short":"Stat Comput","DOI":"10.1007/s11222-023-10329-w","ISSN":"1573-1375","issue":"1","issued":{"date-parts":[["2023",11,7]]},"language":"en","note":"Read_Status: New\nRead_Status_Date: 2025-12-01T12:41:21.757Z","page":"32","source":"Springer Link","title":"Model-based clustering of multiple networks with a hierarchical algorithm","type":"article-journal","URL":"https://doi.org/10.1007/s11222-023-10329-w","volume":"34"},{"id":"RechercheArborescenteMonteCarlo2023","abstract":"En informatique, et plus précisément en intelligence artificielle, la recherche arborescente Monte Carlo ou Monte Carlo tree search (MCTS) est un algorithme de recherche heuristique utilisé dans le cadre de la prise de décision. Il est notamment employé dans les jeux. On peut citer son implémentation dans le jeu vidéo Total War: Rome II avec son mode campagne IA haut-niveau et les récents programmes informatiques de Go, suivis par les échecs et shogi, ainsi que les jeux vidéo en temps réel et les jeux à information incomplète tels que le poker.","accessed":{"date-parts":[["2024",2,9]]},"citation-key":"RechercheArborescenteMonteCarlo2023","container-title":"Wikipédia","issued":{"date-parts":[["2023",5,26]]},"language":"fr","license":"Creative Commons Attribution-ShareAlike License","note":"Page Version ID: 204629004","source":"Wikipedia","title":"Recherche arborescente Monte-Carlo","type":"entry-encyclopedia","URL":"https://fr.wikipedia.org/w/index.php?title=Recherche_arborescente_Monte-Carlo&oldid=204629004"},{"id":"References1990","accessed":{"date-parts":[["2024",9,13]]},"citation-key":"References1990","container-title":"Finding Groups in Data","DOI":"10.1002/9780470316801.refs","ISBN":"978-0-470-31680-1","issued":{"date-parts":[["1990"]]},"language":"en","page":"320–331","publisher":"John Wiley & Sons, Ltd","source":"Wiley Online Library","title":"References","type":"chapter","URL":"https://onlinelibrary.wiley.com/doi/abs/10.1002/9780470316801.refs"},{"id":"reidAssessingSinglelocusCRISPR2022","abstract":"The yellow fever mosquito Aedes aegypti is a major vector of arthropod-borne viruses, including dengue, chikungunya, and Zika viruses. A novel approach to mitigate arboviral infections is to generate mosquitoes refractory to infection by overexpressing antiviral effector molecules. Such an approach requires a mechanism to spread these antiviral effectors through a population, for example, by using CRISPR/Cas9-based gene drive systems. Critical to the design of a single-locus autonomous gene drive is that the selected genomic locus is amenable to both gene drive and appropriate expression of the antiviral effector. In our study, we used reverse engineering to target 2 intergenic genomic loci, which had previously shown to be highly permissive for antiviral effector gene expression, and we further investigated the use of 3 promoters (nanos, β2-tubulin, or zpg) for Cas9 expression. We then quantified the accrual of insertions or deletions (indels) after single-generation crossings, measured maternal effects, and assessed fitness costs associated with various transgenic lines to model the rate of gene drive fixation. Overall, MGDrivE modeling suggested that when an autonomous gene drive is placed into an intergenic locus, the gene drive system will eventually be blocked by the accrual of gene drive blocking resistance alleles and ultimately be lost in the population. Moreover, while genomic locus and promoter selection were critically important for the initial establishment of the autonomous gene drive, it was the fitness of the gene drive line that most strongly influenced the persistence of the gene drive in the simulated population. As such, we propose that when autonomous CRISPR/Cas9-based gene drive systems are anchored in an intergenic locus, they temporarily result in a strong population replacement effect, but as gene drive-blocking indels accrue, the gene drive becomes exhausted due to the fixation of CRISPR resistance alleles.","accessed":{"date-parts":[["2024",9,4]]},"author":[{"family":"Reid","given":"William"},{"family":"Williams","given":"Adeline E"},{"family":"Sanchez-Vargas","given":"Irma"},{"family":"Lin","given":"Jingyi"},{"family":"Juncu","given":"Rucsanda"},{"family":"Olson","given":"Ken E"},{"family":"Franz","given":"Alexander W E"}],"citation-key":"reidAssessingSinglelocusCRISPR2022","container-title":"G3: Genes|Genomes|Genetics","container-title-short":"G3 (Bethesda)","DOI":"10.1093/g3journal/jkac280","ISSN":"2160-1836","issue":"12","issued":{"date-parts":[["2022",10,17]]},"page":"jkac280","PMCID":"PMC9713460","PMID":"36250791","source":"PubMed Central","title":"Assessing single-locus CRISPR/Cas9-based gene drive variants in the mosquito Aedes aegypti via single-generation crosses and modeling","type":"article-journal","URL":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9713460/","volume":"12"},{"id":"RelativeEffectsAnthropogenic","accessed":{"date-parts":[["2026",1,22]]},"citation-key":"RelativeEffectsAnthropogenic","DOI":"10.1111/gcb.15474","language":"en","note":"Read_Status: New\nRead_Status_Date: 2026-01-22T13:04:21.819Z","title":"Relative effects of anthropogenic pressures, climate, and sampling design on the structure of pollination networks at the global scale","type":"webpage","URL":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/gcb.15474"},{"id":"rivera-hutinelEffectsSamplingCompleteness2012","abstract":"Plant–animal interaction networks provide important information on community organization. One of the most critical assumptions of network analysis is that the observed interaction patterns constitute an adequate sample of the set of interactions present in plant–animal communities. In spite of its importance, few studies have evaluated this assumption, and in consequence, there is no consensus on the sensitivity of network metrics to sampling methodological shortcomings. In this study we examined how variation in sampling completeness influences the estimation of six network metrics frequently used in the literature (connectance, nestedness, modularity, robustness to species loss, path length, and centralization). We analyzed data of 186 flowering plants and 336 pollinator species in 10 networks from a forest-fragmented system in central Chile. Using species-based accumulation curves, we estimated the deviation of network metrics in undersampled communities with respect to exhaustively sampled communities and the effect of network size and sampling evenness on network metrics. Our results indicate that: (1) most metrics were affected by sampling completeness but differed in their sensitivity to sampling effort; (2) nestedness, modularity, and robustness to species loss were less influenced by insufficient sampling than connectance, path length, and centralization; (3) robustness was mildly influenced by sampling evenness. These results caution studies that summarize information from databases with high, or unknown, heterogeneity in sampling effort per species and should stimulate researchers to report sampling intensity to standardize its effects in the search for broad patterns in plant–pollinator networks.","accessed":{"date-parts":[["2025",9,18]]},"author":[{"family":"Rivera-Hutinel","given":"A."},{"family":"Bustamante","given":"R. O."},{"family":"Marín","given":"V. H."},{"family":"Medel","given":"R."}],"citation-key":"rivera-hutinelEffectsSamplingCompleteness2012","container-title":"Ecology","DOI":"10.1890/11-1803.1","ISSN":"1939-9170","issue":"7","issued":{"date-parts":[["2012"]]},"language":"en","license":"© 2012 by the Ecological Society of America","note":"Read_Status: New\nRead_Status_Date: 2025-09-18T15:47:50.369Z","page":"1593–1603","source":"Wiley Online Library","title":"Effects of sampling completeness on the structure of plant–pollinator networks","type":"article-journal","URL":"https://onlinelibrary.wiley.com/doi/abs/10.1890/11-1803.1","volume":"93"},{"id":"rizzoNutrientProfilesVegetarian2013","abstract":"Background\nDifferences in nutrient profiles between vegetarian and nonvegetarian dietary patterns reflect nutritional differences that can contribute to the development of disease.\nObjective\nOur aim was to compare nutrient intakes between dietary patterns characterized by consumption or exclusion of meat and dairy products.\nDesign\nWe conducted a cross-sectional study of 71,751 subjects (mean age=59 years) from the Adventist Health Study 2. Data were collected between 2002 and 2007. Participants completed a 204-item validated semi-quantitative food frequency questionnaire. Dietary patterns compared were nonvegetarian, semi-vegetarian, pesco vegetarian, lacto-ovo vegetarian, and strict vegetarian. Analysis of covariance was used to analyze differences in nutrient intakes by dietary patterns and was adjusted for age, sex, and race. Body mass index and other relevant demographic data were reported and compared by dietary pattern using χ2 tests and analysis of variance.\nResults\nMany nutrient intakes varied significantly between dietary patterns. Nonvegetarians had the lowest intakes of plant proteins, fiber, beta carotene, and magnesium compared with those following vegetarian dietary patterns, and the highest intakes of saturated, trans, arachidonic, and docosahexaenoic fatty acids. The lower tails of some nutrient distributions in strict vegetarians suggested inadequate intakes by a portion of the subjects. Energy intake was similar among dietary patterns at close to 2,000 kcal/day, with the exception of semi-vegetarians, who had an intake of 1,707 kcal/day. Mean body mass index was highest in nonvegetarians (mean=28.7 [standard deviation=6.4]) and lowest in strict vegetarians (mean=24.0 [standard deviation=4.8]).\nConclusions\nNutrient profiles varied markedly among dietary patterns that were defined by meat and dairy intakes. These differences are of interest in the etiology of obesity and chronic diseases.","accessed":{"date-parts":[["2024",2,28]]},"author":[{"family":"Rizzo","given":"Nico S."},{"family":"Jaceldo-Siegl","given":"Karen"},{"family":"Sabate","given":"Joan"},{"family":"Fraser","given":"Gary E."}],"citation-key":"rizzoNutrientProfilesVegetarian2013","container-title":"Journal of the Academy of Nutrition and Dietetics","container-title-short":"Journal of the Academy of Nutrition and Dietetics","DOI":"10.1016/j.jand.2013.06.349","ISSN":"2212-2672","issue":"12","issued":{"date-parts":[["2013",12,1]]},"page":"1610–1619","source":"ScienceDirect","title":"Nutrient Profiles of Vegetarian and Nonvegetarian Dietary Patterns","type":"article-journal","URL":"https://www.sciencedirect.com/science/article/pii/S2212267213011131","volume":"113"},{"id":"robertWhyApproximateBayesian","abstract":"Approximate Bayesian computation (ABC), also known as likelihood-free methods, have become a favourite tool for the analysis of complex stochastic models, primarily in population genetics but also in financial analyses. We advocated in Grelaud et al. (2009) the use of ABC for Bayesian model choice in the specific case of Gibbs random fields (GRF), relying on a sufficiency property mainly enjoyed by GRFs to show that the approach was legitimate. Despite having previously suggested the use of ABC for model choice in a wider range of models in the DIY ABC software (Cornuet et al., 2008), we present theoretical evidence that the general use of ABC for model choice is fraught with danger in the sense that no amount of computation, however large, can guarantee a proper approximation of the posterior probabilities of the models under comparison.","author":[{"family":"Robert","given":"Christian P"},{"family":"Marin","given":"Jean-Michel"},{"family":"Pillai","given":"Natesh S"}],"citation-key":"robertWhyApproximateBayesian","language":"en","note":"Read_Status: New\nRead_Status_Date: 2026-04-07T13:23:25.869Z","source":"Zotero","title":"Why approximate Bayesian computational (ABC) methods cannot handle model choice problems","type":"article-journal"},{"id":"robinChangepointDetectionPoisson2019","author":[{"family":"Robin","given":"S"}],"citation-key":"robinChangepointDetectionPoisson2019","issued":{"date-parts":[["2019"]]},"language":"en","source":"Zotero","title":"Change-point detection in a Poisson process","type":"article-journal"},{"id":"rohlfsPhylogeneticANOVAExpression2015","abstract":"A number of methods have been developed for modeling the evolution of a quantitative trait on a phylogeny. These methods have received renewed interest in the context of genome-wide studies of gene expression, in which the expression levels of many genes can be modeled as quantitative traits. We here develop a new method for joint analyses of quantitative traits within- and between species, the Expression Variance and Evolution (EVE) model. The model parameterizes the ratio of population to evolutionary expression variance, facilitating a wide variety of analyses, including a test for lineage-specific shifts in expression level, and a phylogenetic ANOVA that can detect genes with increased or decreased ratios of expression divergence to diversity, analogous to the famous Hudson Kreitman Aguadé (HKA) test used to detect selection at the DNA level. We use simulations to explore the properties of these tests under a variety of circumstances and show that the phylogenetic ANOVA is more accurate than the standard ANOVA (no accounting for phylogeny) sometimes used in transcriptomics. We then apply the EVE model to a mammalian phylogeny of 15 species typed for expression levels in liver tissue. We identify genes with high expression divergence between species as candidates for expression level adaptation, and genes with high expression diversity within species as candidates for expression level conservation and/or plasticity. Using the test for lineage-specific expression shifts, we identify several candidate genes for expression level adaptation on the catarrhine and human lineages, including genes putatively related to dietary changes in humans. We compare these results to those reported previously using a model which ignores expression variance within species, uncovering important differences in performance. We demonstrate the necessity for a phylogenetic model in comparative expression studies and show the utility of the EVE model to detect expression divergence, diversity, and branch-specific shifts.","accessed":{"date-parts":[["2024",3,6]]},"author":[{"family":"Rohlfs","given":"Rori V."},{"family":"Nielsen","given":"Rasmus"}],"citation-key":"rohlfsPhylogeneticANOVAExpression2015","container-title":"Systematic Biology","container-title-short":"Systematic Biology","DOI":"10.1093/sysbio/syv042","ISSN":"1063-5157","issue":"5","issued":{"date-parts":[["2015",9,1]]},"page":"695–708","source":"Silverchair","title":"Phylogenetic ANOVA: The Expression Variance and Evolution Model for Quantitative Trait Evolution","title-short":"Phylogenetic ANOVA","type":"article-journal","URL":"https://doi.org/10.1093/sysbio/syv042","volume":"64"},{"id":"rubin-delanchyStatisticalInterpretationSpectral2022","abstract":"Spectral embedding is a procedure which can be used to obtain vector representations of the nodes of a graph. This paper proposes a generalisation of the latent position network model known as the random dot product graph, to allow interpretation of those vector representations as latent position estimates. The generalisation is needed to model heterophilic connectivity (e.g. ‘opposites attract’) and to cope with negative eigenvalues more generally. We show that, whether the adjacency or normalised Laplacian matrix is used, spectral embedding produces uniformly consistent latent position estimates with asymptotically Gaussian error (up to identifiability). The standard and mixed membership stochastic block models are special cases in which the latent positions take only K distinct vector values, representing communities, or live in the (K − 1)-simplex with those vertices respectively. Under the stochastic block model, our theory suggests spectral clustering using a Gaussian mixture model (rather than K-means) and, under mixed membership, fitting the minimum volume enclosing simplex, existing recommendations previously only supported under non-negative-definite assumptions. Empirical improvements in link prediction (over the random dot product graph), and the potential to uncover richer latent structure (than posited under the standard or mixed membership stochastic block models) are demonstrated in a cyber-security example.","accessed":{"date-parts":[["2025",7,9]]},"author":[{"family":"Rubin-Delanchy","given":"Patrick"},{"family":"Cape","given":"Joshua"},{"family":"Tang","given":"Minh"},{"family":"Priebe","given":"Carey E."}],"citation-key":"rubin-delanchyStatisticalInterpretationSpectral2022","container-title":"Journal of the Royal Statistical Society Series B: Statistical Methodology","container-title-short":"Journal of the Royal Statistical Society Series B: Statistical Methodology","DOI":"10.1111/rssb.12509","ISSN":"1369-7412","issue":"4","issued":{"date-parts":[["2022",9,1]]},"note":"Read_Status: New\nRead_Status_Date: 2025-07-09T14:21:55.886Z","page":"1446–1473","source":"Silverchair","title":"A Statistical Interpretation of Spectral Embedding: The Generalised Random Dot Product Graph","title-short":"A Statistical Interpretation of Spectral Embedding","type":"article-journal","URL":"https://doi.org/10.1111/rssb.12509","volume":"84"},{"id":"sanchez-lengelingGentleIntroductionGraph2021","abstract":"What components are needed for building learning algorithms that leverage the structure and properties of graphs?","accessed":{"date-parts":[["2024",5,15]]},"author":[{"family":"Sanchez-Lengeling","given":"Benjamin"},{"family":"Reif","given":"Emily"},{"family":"Pearce","given":"Adam"},{"family":"Wiltschko","given":"Alexander B."}],"citation-key":"sanchez-lengelingGentleIntroductionGraph2021","container-title":"Distill","container-title-short":"Distill","DOI":"10.23915/distill.00033","ISSN":"2476-0757","issue":"9","issued":{"date-parts":[["2021",9,2]]},"language":"en","page":"e33","source":"distill.pub","title":"A Gentle Introduction to Graph Neural Networks","type":"article-journal","URL":"https://distill.pub/2021/gnn-intro","volume":"6"},{"id":"sanderWhatCanInteraction2015","abstract":"The group model is a useful tool to understand broad-scale patterns of interaction in a network, but it has previously been limited in use to food webs, which contain only predator-prey interactions. Natural populations interact with each other in a ...","accessed":{"date-parts":[["2024",11,4]]},"author":[{"family":"Sander","given":"Elizabeth L."},{"family":"Wootton","given":"J. Timothy"},{"family":"Allesina","given":"Stefano"}],"citation-key":"sanderWhatCanInteraction2015","container-title":"PLoS Computational Biology","DOI":"10.1371/journal.pcbi.1004330","issue":"7","issued":{"date-parts":[["2015",7,21]]},"language":"english","page":"e1004330","PMID":"26197151","title":"What Can Interaction Webs Tell Us About Species Roles?","type":"article-journal","URL":"https://pmc.ncbi.nlm.nih.gov/articles/PMC4511233/","volume":"11"},{"id":"sanderWhatCanInteraction2015a","abstract":"The group model is a useful tool to understand broad-scale patterns of interaction in a network, but it has previously been limited in use to food webs, which contain only predator-prey interactions. Natural populations interact with each other in a ...","accessed":{"date-parts":[["2024",11,4]]},"author":[{"family":"Sander","given":"Elizabeth L."},{"family":"Wootton","given":"J. Timothy"},{"family":"Allesina","given":"Stefano"}],"citation-key":"sanderWhatCanInteraction2015a","container-title":"PLoS Computational Biology","DOI":"10.1371/journal.pcbi.1004330","issue":"7","issued":{"date-parts":[["2015",7,21]]},"language":"en","page":"e1004330","PMID":"26197151","source":"pmc.ncbi.nlm.nih.gov","title":"What Can Interaction Webs Tell Us About Species Roles?","type":"article-journal","URL":"https://pmc.ncbi.nlm.nih.gov/articles/PMC4511233/","volume":"11"},{"id":"satterthwaiteApproximateDistributionEstimates1946","accessed":{"date-parts":[["2024",1,8]]},"author":[{"family":"Satterthwaite","given":"F. E."}],"citation-key":"satterthwaiteApproximateDistributionEstimates1946","container-title":"Biometrics Bulletin","container-title-short":"Biometrics Bulletin","DOI":"10.2307/3002019","ISSN":"00994987","issue":"6","issued":{"date-parts":[["1946",12]]},"page":"110","source":"DOI.org (Crossref)","title":"An Approximate Distribution of Estimates of Variance Components","type":"article-journal","URL":"https://www.jstor.org/stable/10.2307/3002019?origin=crossref","volume":"2"},{"id":"schaefferDoubleDescentDemystified2023","abstract":"Double descent is a surprising phenomenon in machine learning, in which as the number of model parameters grows relative to the number of data, test error drops as models grow ever larger into the highly overparameterized (data undersampled) regime. This drop in test error flies against classical learning theory on overfitting and has arguably underpinned the success of large models in machine learning. This non-monotonic behavior of test loss depends on the number of data, the dimensionality of the data and the number of model parameters. Here, we briefly describe double descent, then provide an explanation of why double descent occurs in an informal and approachable manner, requiring only familiarity with linear algebra and introductory probability. We provide visual intuition using polynomial regression, then mathematically analyze double descent with ordinary linear regression and identify three interpretable factors that, when simultaneously all present, together create double descent. We demonstrate that double descent occurs on real data when using ordinary linear regression, then demonstrate that double descent does not occur when any of the three factors are ablated. We use this understanding to shed light on recent observations in nonlinear models concerning superposition and double descent. Code is publicly available.","accessed":{"date-parts":[["2025",10,20]]},"author":[{"family":"Schaeffer","given":"Rylan"},{"family":"Khona","given":"Mikail"},{"family":"Robertson","given":"Zachary"},{"family":"Boopathy","given":"Akhilan"},{"family":"Pistunova","given":"Kateryna"},{"family":"Rocks","given":"Jason W."},{"family":"Fiete","given":"Ila Rani"},{"family":"Koyejo","given":"Oluwasanmi"}],"citation-key":"schaefferDoubleDescentDemystified2023","DOI":"10.48550/arXiv.2303.14151","issued":{"date-parts":[["2023",3,24]]},"note":"Read_Status: New\nRead_Status_Date: 2025-10-20T18:20:29.013Z","number":"arXiv:2303.14151","publisher":"arXiv","source":"arXiv.org","title":"Double Descent Demystified: Identifying, Interpreting & Ablating the Sources of a Deep Learning Puzzle","title-short":"Double Descent Demystified","type":"article","URL":"http://arxiv.org/abs/2303.14151"},{"id":"schrittwieserMasteringAtariGo2020","abstract":"Constructing agents with planning capabilities has long been one of the main challenges in the pursuit of artificial intelligence. Tree-based planning methods have enjoyed huge success in challenging domains, such as chess and Go, where a perfect simulator is available. However, in real-world problems the dynamics governing the environment are often complex and unknown. In this work we present the MuZero algorithm which, by combining a tree-based search with a learned model, achieves superhuman performance in a range of challenging and visually complex domains, without any knowledge of their underlying dynamics. MuZero learns a model that, when applied iteratively, predicts the quantities most directly relevant to planning: the reward, the action-selection policy, and the value function. When evaluated on 57 different Atari games - the canonical video game environment for testing AI techniques, in which model-based planning approaches have historically struggled - our new algorithm achieved a new state of the art. When evaluated on Go, chess and shogi, without any knowledge of the game rules, MuZero matched the superhuman performance of the AlphaZero algorithm that was supplied with the game rules.","accessed":{"date-parts":[["2024",2,8]]},"author":[{"family":"Schrittwieser","given":"Julian"},{"family":"Antonoglou","given":"Ioannis"},{"family":"Hubert","given":"Thomas"},{"family":"Simonyan","given":"Karen"},{"family":"Sifre","given":"Laurent"},{"family":"Schmitt","given":"Simon"},{"family":"Guez","given":"Arthur"},{"family":"Lockhart","given":"Edward"},{"family":"Hassabis","given":"Demis"},{"family":"Graepel","given":"Thore"},{"family":"Lillicrap","given":"Timothy"},{"family":"Silver","given":"David"}],"citation-key":"schrittwieserMasteringAtariGo2020","container-title":"Nature","container-title-short":"Nature","DOI":"10.1038/s41586-020-03051-4","ISSN":"0028-0836, 1476-4687","issue":"7839","issued":{"date-parts":[["2020",12,24]]},"language":"en","page":"604–609","source":"arXiv.org","title":"Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model","type":"article-journal","URL":"http://arxiv.org/abs/1911.08265","volume":"588"},{"id":"schwarzEstimatingDimensionModel1978","abstract":"The problem of selecting one of a number of models of different dimensions is treated by finding its Bayes solution, and evaluating the leading terms of its asymptotic expansion. These terms are a valid large-sample criterion beyond the Bayesian context, since they do not depend on the a priori distribution.","accessed":{"date-parts":[["2025",1,29]]},"author":[{"family":"Schwarz","given":"Gideon"}],"citation-key":"schwarzEstimatingDimensionModel1978","container-title":"The Annals of Statistics","DOI":"10.1214/aos/1176344136","ISSN":"0090-5364, 2168-8966","issue":"2","issued":{"date-parts":[["1978",3]]},"page":"461–464","publisher":"Institute of Mathematical Statistics","source":"Project Euclid","title":"Estimating the Dimension of a Model","type":"article-journal","URL":"https://projecteuclid.org/journals/annals-of-statistics/volume-6/issue-2/Estimating-the-Dimension-of-a-Model/10.1214/aos/1176344136.full","volume":"6"},{"id":"scutariIntroductionGraphicalModelling2011","abstract":"The aim of this chapter is twofold. In the first part we will provide a brief overview of the mathematical and statistical foundations of graphical models, along with their fundamental properties, estimation and basic inference procedures. In particular we will develop Markov networks (also known as Markov random fields) and Bayesian networks, which comprise most past and current literature on graphical models. In the second part we will review some applications of graphical models in systems biology.","accessed":{"date-parts":[["2025",10,14]]},"author":[{"family":"Scutari","given":"Marco"},{"family":"Strimmer","given":"Korbinian"}],"citation-key":"scutariIntroductionGraphicalModelling2011","DOI":"10.48550/arXiv.1005.1036","issued":{"date-parts":[["2011",6,28]]},"note":"Read_Status: New\nRead_Status_Date: 2025-10-14T15:23:23.472Z","number":"arXiv:1005.1036","publisher":"arXiv","source":"arXiv.org","title":"Introduction to Graphical Modelling","type":"article","URL":"http://arxiv.org/abs/1005.1036"},{"id":"sewellLatentSpaceModels2015","accessed":{"date-parts":[["2024",5,20]]},"author":[{"family":"Sewell","given":"Daniel K."},{"family":"Chen","given":"Yuguo"}],"citation-key":"sewellLatentSpaceModels2015","container-title":"Journal of the American Statistical Association","DOI":"10.1080/01621459.2014.988214","ISSN":"0162-1459","issue":"512","issued":{"date-parts":[["2015",10,2]]},"page":"1646–1657","publisher":"Taylor & Francis","source":"tandfonline.com (Atypon)","title":"Latent Space Models for Dynamic Networks","type":"article-journal","URL":"https://www.tandfonline.com/doi/full/10.1080/01621459.2014.988214","volume":"110"},{"id":"sharmaDeepDiveVariational2023","abstract":"Explore Variational Autoencoders: Understand basics, compare with Convolutional Autoencoders, and train on Fashion-MNIST. A complete guide.","accessed":{"date-parts":[["2024",2,19]]},"author":[{"family":"Sharma","given":"Aditya"}],"citation-key":"sharmaDeepDiveVariational2023","container-title":"PyImageSearch","issued":{"date-parts":[["2023",10,2]]},"language":"en-US","title":"A Deep Dive into Variational Autoencoders with PyTorch","type":"post-weblog","URL":"https://pyimagesearch.com/2023/10/02/a-deep-dive-into-variational-autoencoders-with-pytorch/"},{"id":"shervashidzeEfficientGraphletKernels2009","abstract":"State-of-the-art graph kernels do not scale to large graphs with hundreds of nodes and thousands of edges. In this article we propose to compare graphs by counting graphlets, i.e., subgraphs with kkk nodes where k∈{3,4,5}k∈{3,4,5}k \\in \\{ 3, 4, 5 \\}. Exhaustive enumeration of all graphlets being prohibitively expensive, we introduce two theoretically grounded speedup schemes, one based on sampling and the second one specifically designed for bounded degree graphs. In our experimental evaluation, our novel kernels allow us to efficiently compare large graphs that cannot be tackled by existing graph kernels.","accessed":{"date-parts":[["2025",1,26]]},"author":[{"family":"Shervashidze","given":"Nino"},{"family":"Vishwanathan","given":"S. V. N."},{"family":"Petri","given":"Tobias"},{"family":"Mehlhorn","given":"Kurt"},{"family":"Borgwardt","given":"Karsten"}],"citation-key":"shervashidzeEfficientGraphletKernels2009","container-title":"Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics","event-title":"Artificial Intelligence and Statistics","ISSN":"1938-7228","issued":{"date-parts":[["2009",4,15]]},"language":"en","page":"488–495","publisher":"PMLR","source":"proceedings.mlr.press","title":"Efficient graphlet kernels for large graph comparison","type":"paper-conference","URL":"https://proceedings.mlr.press/v5/shervashidze09a.html"},{"id":"shervashidzeWeisfeilerLehmanGraphKernels2011","abstract":"In this article, we propose a family of efficient kernels for large graphs with discrete node labels. Key to our method is a rapid feature extraction scheme based on the Weisfeiler-Lehman test of isomorphism on graphs. It maps the original graph to a sequence of graphs, whose node attributes capture topological and label information. A family of kernels can be defined based on this Weisfeiler-Lehman sequence of graphs, including a highly efficient kernel comparing subtree-like patterns. Its runtime scales only linearly in the number of edges of the graphs and the length of the Weisfeiler-Lehman graph sequence. In our experimental evaluation, our kernels outperform state-of-the-art graph kernels on several graph classification benchmark data sets in terms of accuracy and runtime. Our kernels open the door to large-scale applications of graph kernels in various disciplines such as computational biology and social network analysis.","accessed":{"date-parts":[["2025",1,26]]},"author":[{"family":"Shervashidze","given":"Nino"},{"family":"Schweitzer","given":"Pascal"},{"family":"Leeuwen","given":"Erik Jan","dropping-particle":"van"},{"family":"Mehlhorn","given":"Kurt"},{"family":"Borgwardt","given":"Karsten M."}],"citation-key":"shervashidzeWeisfeilerLehmanGraphKernels2011","container-title":"Journal of Machine Learning Research","ISSN":"1533-7928","issue":"77","issued":{"date-parts":[["2011"]]},"page":"2539–2561","source":"jmlr.csail.mit.edu","title":"Weisfeiler-Lehman Graph Kernels","type":"article-journal","URL":"http://jmlr.org/papers/v12/shervashidze11a.html","volume":"12"},{"id":"sheykhaliRobustnessExtinctionPlasticity2020","abstract":"Understanding the response of ecological networks to perturbations and disruptive events is needed to anticipate the biodiversity loss and extinction cascades. Here, we study how network plasticity reshapes the topology of mutualistic networks in response to species loss. We analyze more than one hundred empirical mutualistic networks and considered random and targeted removal as mechanisms of species extinction. Network plasticity is modeled as either random rewiring, as the most parsimonious approach, or resource affinity-driven rewiring, as a proxy for encoding the phylogenetic similarity and functional redundancy among species. This redundancy should be positively correlated with the robustness of an ecosystem, as functions can be taken by other species once one of them is extinct. We show that effective modularity, i.e. the ability of an ecosystem to adapt or restructure, increases with increasing numbers of extinctions, and with decreasing the replacement probability. Importantly, modularity is mostly affected by the extinction rather than by rewiring mechanisms. These changes in community structure are reflected in the robustness and stability due to their positive correlation with modularity. Resource affinity-driven rewiring offers an increase of modularity, robustness, and stability which could be an evolutionary favored mechanism to prevent a cascade of co-extinctions.","accessed":{"date-parts":[["2025",9,18]]},"author":[{"family":"Sheykhali","given":"Somaye"},{"family":"Fernández-Gracia","given":"Juan"},{"family":"Traveset","given":"Anna"},{"family":"Ziegler","given":"Maren"},{"family":"Voolstra","given":"Christian R."},{"family":"Duarte","given":"Carlos M."},{"family":"Eguíluz","given":"Víctor M."}],"citation-key":"sheykhaliRobustnessExtinctionPlasticity2020","container-title":"Scientific reports","container-title-short":"Sci Rep","DOI":"10.1038/s41598-020-66131-5","ISSN":"2045-2322","issue":"1","issued":{"date-parts":[["2020",6,17]]},"language":"english","page":"9783","publisher":"Nature Publishing Group","title":"Robustness to extinction and plasticity derived from mutualistic bipartite ecological networks","type":"article-journal","URL":"https://www.nature.com/articles/s41598-020-66131-5","volume":"10"},{"id":"sheykhaliRobustnessExtinctionPlasticity2020a","abstract":"Understanding the response of ecological networks to perturbations and disruptive events is needed to anticipate the biodiversity loss and extinction cascades. Here, we study how network plasticity reshapes the topology of mutualistic networks in response to species loss. We analyze more than one hundred empirical mutualistic networks and considered random and targeted removal as mechanisms of species extinction. Network plasticity is modeled as either random rewiring, as the most parsimonious approach, or resource affinity-driven rewiring, as a proxy for encoding the phylogenetic similarity and functional redundancy among species. This redundancy should be positively correlated with the robustness of an ecosystem, as functions can be taken by other species once one of them is extinct. We show that effective modularity, i.e. the ability of an ecosystem to adapt or restructure, increases with increasing numbers of extinctions, and with decreasing the replacement probability. Importantly, modularity is mostly affected by the extinction rather than by rewiring mechanisms. These changes in community structure are reflected in the robustness and stability due to their positive correlation with modularity. Resource affinity-driven rewiring offers an increase of modularity, robustness, and stability which could be an evolutionary favored mechanism to prevent a cascade of co-extinctions.","accessed":{"date-parts":[["2025",9,18]]},"author":[{"family":"Sheykhali","given":"Somaye"},{"family":"Fernández-Gracia","given":"Juan"},{"family":"Traveset","given":"Anna"},{"family":"Ziegler","given":"Maren"},{"family":"Voolstra","given":"Christian R."},{"family":"Duarte","given":"Carlos M."},{"family":"Eguíluz","given":"Víctor M."}],"citation-key":"sheykhaliRobustnessExtinctionPlasticity2020a","container-title":"Scientific Reports","container-title-short":"Sci Rep","DOI":"10.1038/s41598-020-66131-5","ISSN":"2045-2322","issue":"1","issued":{"date-parts":[["2020",6,17]]},"language":"en","license":"2020 The Author(s)","note":"Read_Status: New\nRead_Status_Date: 2025-09-18T14:43:11.755Z","page":"9783","publisher":"Nature Publishing Group","source":"www.nature.com","title":"Robustness to extinction and plasticity derived from mutualistic bipartite ecological networks","type":"article-journal","URL":"https://www.nature.com/articles/s41598-020-66131-5","volume":"10"},{"id":"Shogi2023","abstract":"Le shōgi (将棋, littéralement « jeu des généraux ») est un jeu de société combinatoire abstrait traditionnel japonais, se rapprochant du jeu d'échecs, et opposant deux joueurs.\nCe jeu est célébré le 17 novembre au Japon, où il est extrêmement populaire.","accessed":{"date-parts":[["2024",2,5]]},"citation-key":"Shogi2023","container-title":"Wikipédia","issued":{"date-parts":[["2023",10,12]]},"language":"fr","license":"Creative Commons Attribution-ShareAlike License","note":"Page Version ID: 208644993","source":"Wikipedia","title":"Shōgi","type":"entry-encyclopedia","URL":"https://fr.wikipedia.org/w/index.php?title=Sh%C5%8Dgi&oldid=208644993"},{"id":"silverMasteringChessShogi2017","abstract":"The game of chess is the most widely-studied domain in the history of artificial intelligence. The strongest programs are based on a combination of sophisticated search techniques, domain-specific adaptations, and handcrafted evaluation functions that have been refined by human experts over several decades. In contrast, the AlphaGo Zero program recently achieved superhuman performance in the game of Go, by tabula rasa reinforcement learning from games of self-play. In this paper, we generalise this approach into a single AlphaZero algorithm that can achieve, tabula rasa, superhuman performance in many challenging domains. Starting from random play, and given no domain knowledge except the game rules, AlphaZero achieved within 24 hours a superhuman level of play in the games of chess and shogi (Japanese chess) as well as Go, and convincingly defeated a world-champion program in each case.","accessed":{"date-parts":[["2024",2,5]]},"author":[{"family":"Silver","given":"David"},{"family":"Hubert","given":"Thomas"},{"family":"Schrittwieser","given":"Julian"},{"family":"Antonoglou","given":"Ioannis"},{"family":"Lai","given":"Matthew"},{"family":"Guez","given":"Arthur"},{"family":"Lanctot","given":"Marc"},{"family":"Sifre","given":"Laurent"},{"family":"Kumaran","given":"Dharshan"},{"family":"Graepel","given":"Thore"},{"family":"Lillicrap","given":"Timothy"},{"family":"Simonyan","given":"Karen"},{"family":"Hassabis","given":"Demis"}],"citation-key":"silverMasteringChessShogi2017","DOI":"10.48550/arXiv.1712.01815","issued":{"date-parts":[["2017",12,5]]},"number":"arXiv:1712.01815","publisher":"arXiv","source":"arXiv.org","title":"Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm","type":"article","URL":"http://arxiv.org/abs/1712.01815"},{"id":"silverMasteringGameGo2017","abstract":"A long-standing goal of artificial intelligence is an algorithm that learns, tabula rasa, superhuman proficiency in challenging domains. Recently, AlphaGo became the first program to defeat a world champion in the game of Go. The tree search in AlphaGo evaluated positions and selected moves using deep neural networks. These neural networks were trained by supervised learning from human expert moves, and by reinforcement learning from self-play. Here we introduce an algorithm based solely on reinforcement learning, without human data, guidance or domain knowledge beyond game rules. AlphaGo becomes its own teacher: a neural network is trained to predict AlphaGo’s own move selections and also the winner of AlphaGo’s games. This neural network improves the strength of the tree search, resulting in higher quality move selection and stronger self-play in the next iteration. Starting tabula rasa, our new program AlphaGo Zero achieved superhuman performance, winning 100–0 against the previously published, champion-defeating AlphaGo. Starting from zero knowledge and without human data, AlphaGo Zero was able to teach itself to play Go and to develop novel strategies that provide new insights into the oldest of games. To beat world champions at the game of Go, the computer program AlphaGo has relied largely on supervised learning from millions of human expert moves. David Silver and colleagues have now produced a system called AlphaGo Zero, which is based purely on reinforcement learning and learns solely from self-play. Starting from random moves, it can reach superhuman level in just a couple of days of training and five million games of self-play, and can now beat all previous versions of AlphaGo. Because the machine independently discovers the same fundamental principles of the game that took humans millennia to conceptualize, the work suggests that such principles have some universal character, beyond human bias.","accessed":{"date-parts":[["2024",2,14]]},"author":[{"family":"Silver","given":"David"},{"family":"Schrittwieser","given":"Julian"},{"family":"Simonyan","given":"Karen"},{"family":"Antonoglou","given":"Ioannis"},{"family":"Huang","given":"Aja"},{"family":"Guez","given":"Arthur"},{"family":"Hubert","given":"Thomas"},{"family":"Baker","given":"Lucas"},{"family":"Lai","given":"Matthew"},{"family":"Bolton","given":"Adrian"},{"family":"Chen","given":"Yutian"},{"family":"Lillicrap","given":"Timothy"},{"family":"Hui","given":"Fan"},{"family":"Sifre","given":"Laurent"},{"family":"Driessche","given":"George","non-dropping-particle":"van den"},{"family":"Graepel","given":"Thore"},{"family":"Hassabis","given":"Demis"}],"citation-key":"silverMasteringGameGo2017","container-title":"Nature","DOI":"10.1038/nature24270","ISSN":"1476-4687","issue":"7676","issued":{"date-parts":[["2017",10]]},"language":"en","license":"2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.","number":"7676","page":"354–359","publisher":"Nature Publishing Group","source":"www.nature.com","title":"Mastering the game of Go without human knowledge","type":"article-journal","URL":"https://www.nature.com/articles/nature24270","volume":"550"},{"id":"simmonsMotifsBipartiteEcological2019","abstract":"Indirect interactions play an essential role in governing population, community and coevolutionary dynamics across a diverse range of ecological communities. Such communities are widely represented as bipartite networks: graphs depicting interactions between two groups of species, such as plants and pollinators or hosts and parasites. For over thirty years, studies have used indices, such as connectance and species degree, to characterise the structure of these networks and the roles of their constituent species. However, compressing a complex network into a single metric necessarily discards large amounts of information about indirect interactions. Given the large literature demonstrating the importance and ubiquity of indirect effects, many studies of network structure are likely missing a substantial piece of the ecological puzzle. Here we use the emerging concept of bipartite motifs to outline a new framework for bipartite networks that incorporates indirect interactions. While this framework is a significant departure from the current way of thinking about bipartite ecological networks, we show that this shift is supported by analyses of simulated and empirical data. We use simulations to show how consideration of indirect interactions can highlight differences missed by the current index paradigm that may be ecologically important. We extend this finding to empirical plant–pollinator communities, showing how two bee species, with similar direct interactions, differ in how specialised their competitors are. These examples underscore the need to not rely solely on network‐ and species‐level indices for characterising the structure of bipartite ecological networks.","accessed":{"date-parts":[["2025",4,10]]},"author":[{"family":"Simmons","given":"Benno I."},{"family":"Cirtwill","given":"Alyssa R."},{"family":"Baker","given":"Nick J."},{"family":"Wauchope","given":"Hannah S."},{"family":"Dicks","given":"Lynn V."},{"family":"Stouffer","given":"Daniel B."},{"family":"Sutherland","given":"William J."}],"citation-key":"simmonsMotifsBipartiteEcological2019","container-title":"Oikos","container-title-short":"Oikos","DOI":"10.1111/oik.05670","ISSN":"0030-1299, 1600-0706","issue":"2","issued":{"date-parts":[["2019",1]]},"language":"en","page":"154–170","source":"DOI.org (Crossref)","title":"Motifs in bipartite ecological networks: uncovering indirect interactions","title-short":"Motifs in bipartite ecological networks","type":"article-journal","URL":"https://onlinelibrary.wiley.com/doi/10.1111/oik.05670","volume":"128"},{"id":"SimpleAlphaZero","accessed":{"date-parts":[["2024",2,8]]},"citation-key":"SimpleAlphaZero","title":"Simple Alpha Zero","type":"webpage","URL":"https://web.stanford.edu/~surag/posts/alphazero.html"},{"id":"smolenRightTimeLearn2016","accessed":{"date-parts":[["2024",12,25]]},"author":[{"family":"Smolen","given":"Paul"},{"family":"Zhang","given":"Yili"},{"family":"Byrne","given":"John H."}],"citation-key":"smolenRightTimeLearn2016","container-title":"Nature Reviews Neuroscience","container-title-short":"Nat Rev Neurosci","DOI":"10.1038/nrn.2015.18","ISSN":"1471-003X, 1471-0048","issue":"2","issued":{"date-parts":[["2016",2]]},"language":"en","page":"77–88","source":"DOI.org (Crossref)","title":"The right time to learn: mechanisms and optimization of spaced learning","title-short":"The right time to learn","type":"article-journal","URL":"https://www.nature.com/articles/nrn.2015.18","volume":"17"},{"id":"snijdersEstimationPredictionStochastic1997","abstract":"blockmodeling for graphs is proposed. The model assumes that the vertices of the graph are partitioned into two unknown blocks and that the probability of an edge between two vertices depends only on the blocks to which they belong. Statistical procedures are derived for estimating the probabilities of edges and for predicting the block structure from observations of the edge pattern only. ML estimators can be computed using the EM algorithm, but this strategy is practical only for small graphs. A Bayesian estimator, based on the Gibbs sampling, is proposed. This estimator is practical also for large graphs. When ML estimators are used, the block structure can be predicted based on predictive likelihood. When Gibbs sampling is used, the block structure can be predicted from posterior predictive probabilities. A side result is that when the number of vertices tends to infinity while the probabilities remain constant, the block structure can be recovered correctly with probability tending to 1.","accessed":{"date-parts":[["2023",6,15]]},"author":[{"family":"Snijders","given":"Tom A.B."},{"family":"Nowicki","given":"Krzysztof"}],"citation-key":"snijdersEstimationPredictionStochastic1997","container-title":"Journal of Classification","container-title-short":"J. of Classification","DOI":"10.1007/s003579900004","ISSN":"1432-1343","issue":"1","issued":{"date-parts":[["1997",1,1]]},"language":"en","page":"75–100","source":"Springer Link","title":"Estimation and Prediction for Stochastic Blockmodels for Graphs with Latent Block Structure","type":"article-journal","URL":"https://doi.org/10.1007/s003579900004","volume":"14"},{"id":"soubeyrandDonneesTheseExperience2024","author":[{"family":"Soubeyrand","given":"Lola"}],"citation-key":"soubeyrandDonneesTheseExperience2024","issued":{"date-parts":[["2024",2,27]]},"medium":"csv","title":"Données de thèse, expérience prise de décisions sous incertitude","type":"dataset"},{"id":"souzaTemporalVariationPlant2018","abstract":"The temporal dynamics of plant phenology and pollinator abundance across seasons should influence the structure of plant–pollinator interaction networks. Nevertheless, such dynamics are seldom considered, especially for diverse tropical networks. Here, we evaluated the temporal variation of four plant–pollinator networks in two seasonal ecosystems in Central Brazil (Cerrado and Pantanal). Data were gathered on a monthly basis over 1 year for each network. We characterized seasonal and temporal shifts in plant–pollinator interactions, using temporally discrete networks. We predicted that the greater floral availability in the rainy season would allow for finer partitioning of the floral niche by the pollinators, i.e. higher specialization patterns as previously described across large spatial gradients. Finally, we also evaluated how sampling restricted to peak flowering period may affect the characterization of the networks. Contrary to our expectations, we found that dry season networks, although characterized by lower floral resource richness and abundance, showed higher levels of network-wide interaction partitioning (complementary specialization and modularity). For nestedness, though, this between-seasons difference was not consistent. Reduced resource availability in the dry season may promote higher interspecific competition among pollinators leading to reduced niche overlap, thus explaining the increase in specialization. There were no consistent differences between seasons in species-level indices, indicating that higher network level specialization is an emergent property only seen when considering the entire network. However, bees presented higher values of specialization and species strength in relation to other groups such as flies and wasps, suggesting that some plant species frequently associated with bees are used only by this group. Our study also indicates that targeted data collection during peak flowering generates higher estimates of network specialization, possibly because species activity spans longer periods than the targeted time frame. Hence, depending on the period of data collection, different structural values for the networks of interactions may be found. Synthesis. Plant–pollinator networks from tropical environments have structural properties that vary according to seasons, which should be taken into account in the description of the complex systems of interactions between plants and their pollinators in these areas.","accessed":{"date-parts":[["2025",3,24]]},"author":[{"family":"Souza","given":"Camila S."},{"family":"Maruyama","given":"Pietro K."},{"family":"Aoki","given":"Camila"},{"family":"Sigrist","given":"Maria R."},{"family":"Raizer","given":"Josué"},{"family":"Gross","given":"Caroline L."},{"family":"Araujo","given":"Andréa C.","non-dropping-particle":"de"}],"citation-key":"souzaTemporalVariationPlant2018","container-title":"Journal of Ecology","DOI":"10.1111/1365-2745.12978","ISSN":"1365-2745","issue":"6","issued":{"date-parts":[["2018"]]},"language":"en","license":"© 2018 The Authors. Journal of Ecology © 2018 British Ecological Society","page":"2409–2420","source":"Wiley Online Library","title":"Temporal variation in plant–pollinator networks from seasonal tropical environments: Higher specialization when resources are scarce","title-short":"Temporal variation in plant–pollinator networks from seasonal tropical environments","type":"article-journal","URL":"https://onlinelibrary.wiley.com/doi/abs/10.1111/1365-2745.12978","volume":"106"},{"id":"SubjectIndex1990","accessed":{"date-parts":[["2024",9,13]]},"citation-key":"SubjectIndex1990","container-title":"Finding Groups in Data","DOI":"10.1002/9780470316801.indsub","ISBN":"978-0-470-31680-1","issued":{"date-parts":[["1990"]]},"language":"en","page":"335–342","publisher":"John Wiley & Sons, Ltd","source":"Wiley Online Library","title":"Subject Index","type":"chapter","URL":"https://onlinelibrary.wiley.com/doi/abs/10.1002/9780470316801.indsub"},{"id":"Sujetthese","citation-key":"Sujetthese","title":"sujet-these","type":"document"},{"id":"suttonReinforcementLearningIntroduction2015","author":[{"family":"Sutton","given":"Richard S"},{"family":"Barto","given":"Andrew G"}],"citation-key":"suttonReinforcementLearningIntroduction2015","edition":"2nde Édition","issued":{"date-parts":[["2015"]]},"language":"en","source":"Zotero","title":"Reinforcement Learning: An Introduction","type":"book"},{"id":"TabulaRasaReincarnating2022","accessed":{"date-parts":[["2024",2,5]]},"citation-key":"TabulaRasaReincarnating2022","issued":{"date-parts":[["2022",11,3]]},"language":"en","title":"Beyond Tabula Rasa: Reincarnating Reinforcement Learning","title-short":"Beyond Tabula Rasa","type":"webpage","URL":"https://blog.research.google/2022/11/beyond-tabula-rasa-reincarnating.html"},{"id":"thebaultDatabasePlantpollinatorNetworks2020","abstract":"This database assembles different published datasets of observed interaction networks between plants and pollinators, which were extracted from articles, theses and existing online databases. Each row in the data table corresponds to an interaction between a plant and a pollinator species reported at a given site by a given publication.","accessed":{"date-parts":[["2023",6,21]]},"author":[{"family":"Thébault","given":"Elisa"},{"family":"Fontaine","given":"Colin"}],"citation-key":"thebaultDatabasePlantpollinatorNetworks2020","DOI":"10.5281/zenodo.4300427","issued":{"date-parts":[["2020",12,1]]},"publisher":"Zenodo","source":"Zenodo","title":"A database of plant-pollinator networks","type":"dataset","URL":"https://zenodo.org/record/4300427","version":"1"},{"id":"thebaultelisaDatabasePlantpollinatorNetworks2020","abstract":"This database assembles different published datasets of observed interaction networks between plants and pollinators, which were extracted from articles, theses and existing online databases. Each row in the data table corresponds to an interaction between a plant and a pollinator species reported at a given site by a given publication.","accessed":{"date-parts":[["2023",6,21]]},"author":[{"family":"Thébault, Elisa","given":""},{"family":"Fontaine, Colin","given":""}],"citation-key":"thebaultelisaDatabasePlantpollinatorNetworks2020","DOI":"10.5281/ZENODO.4300427","issued":{"date-parts":[["2020",12,1]]},"license":"Creative Commons Attribution 4.0 International, Open Access","publisher":"Zenodo","source":"DOI.org (Datacite)","title":"A database of plant-pollinator networks","type":"dataset","URL":"https://zenodo.org/record/4300427","version":"1"},{"id":"thebaultelisaDatabasePlantpollinatorNetworks2022","abstract":"This database assembles different published datasets of observed interaction networks between plants and pollinators, which were extracted from articles, theses and existing online databases. Each row in the data table corresponds to an interaction between a plant and a pollinator species reported at a given site by a given publication.","accessed":{"date-parts":[["2023",6,21]]},"author":[{"family":"Thébault, Elisa","given":""},{"family":"Fontaine, Colin","given":""}],"citation-key":"thebaultelisaDatabasePlantpollinatorNetworks2022","contributor":[{"family":"Doré, Maël","given":""},{"family":"Parra, Santiago","given":""}],"DOI":"10.5281/ZENODO.4300426","issued":{"date-parts":[["2022",6,10]]},"license":"Creative Commons Attribution 4.0 International, Open Access","publisher":"Zenodo","source":"DOI.org (Datacite)","title":"A database of plant-pollinator networks","type":"dataset","URL":"https://zenodo.org/record/4300426","version":"2"},{"id":"thebaultelisaDatabasePlantpollinatorNetworks2022a","abstract":"This database assembles different published datasets of observed interaction networks between plants and pollinators, which were extracted from articles, theses and existing online databases. Each row in the data table corresponds to an interaction between a plant and a pollinator species reported at a given site by a given publication.","accessed":{"date-parts":[["2023",6,21]]},"author":[{"family":"Thébault, Elisa","given":""},{"family":"Fontaine, Colin","given":""}],"citation-key":"thebaultelisaDatabasePlantpollinatorNetworks2022a","contributor":[{"family":"Doré, Maël","given":""},{"family":"Parra, Santiago","given":""}],"DOI":"10.5281/ZENODO.6630184","issued":{"date-parts":[["2022",6,10]]},"license":"Creative Commons Attribution 4.0 International, Open Access","publisher":"Zenodo","source":"DOI.org (Datacite)","title":"A database of plant-pollinator networks","type":"dataset","URL":"https://zenodo.org/record/6630184","version":"2"},{"id":"tisseauTikZPourLimpatient","author":[{"family":"Tisseau","given":"Gérard"},{"family":"Duma","given":"Jacques"}],"citation-key":"tisseauTikZPourLimpatient","language":"fr","note":"Read_Status: New\nRead_Status_Date: 2025-10-27T10:07:43.523Z","source":"Zotero","title":"TikZ pour l'impatient","type":"article-journal"},{"id":"togninalliWassersteinWeisfeilerLehmanGraph2019","abstract":"Most graph kernels are an instance of the class of R-Convolution kernels, which measure the similarity of objects by comparing their substructures.\nDespite their empirical success, most graph kernels use a naive aggregation of the final set of substructures, usually a sum or average, thereby potentially discarding valuable information about the distribution of individual components. Furthermore, only a limited instance of these approaches can be extended to continuously attributed graphs. \nWe propose a novel method that relies on the Wasserstein distance between the node feature vector distributions of two graphs, which allows to find subtler differences in data sets by considering graphs as high-dimensional objects, rather than simple means.\nWe further propose a Weisfeiler--Lehman inspired embedding scheme for graphs with continuous node attributes and weighted edges, enhance it with the computed Wasserstein distance, and thus improve the state-of-the-art prediction performance on several graph classification tasks.","accessed":{"date-parts":[["2025",1,26]]},"author":[{"family":"Togninalli","given":"Matteo"},{"family":"Ghisu","given":"Elisabetta"},{"family":"Llinares-López","given":"Felipe"},{"family":"Rieck","given":"Bastian"},{"family":"Borgwardt","given":"Karsten"}],"citation-key":"togninalliWassersteinWeisfeilerLehmanGraph2019","container-title":"Advances in Neural Information Processing Systems","issued":{"date-parts":[["2019"]]},"publisher":"Curran Associates, Inc.","source":"Neural Information Processing Systems","title":"Wasserstein Weisfeiler-Lehman Graph Kernels","type":"paper-conference","URL":"https://proceedings.neurips.cc/paper_files/paper/2019/hash/73fed7fd472e502d8908794430511f4d-Abstract.html","volume":"32"},{"id":"TP1Sciences","citation-key":"TP1Sciences","language":"fr","source":"Zotero","title":"TP 1 de Sciences des données : apprentissage statistique","type":"article-journal"},{"id":"TP2Sciences","citation-key":"TP2Sciences","container-title":"' '","language":"fr","source":"Zotero","title":"TP 2 de Sciences des données : apprentissage statistique (éléments de correction)","type":"article-journal"},{"id":"TP3Sciences","citation-key":"TP3Sciences","language":"fr","source":"Zotero","title":"TP 3 de Sciences des données : apprentissage statistique","type":"article-journal"},{"id":"trojelsgaardMacroecologyPollinationNetworks2013","abstract":"Aim Interacting communities of species are organized into complex networks, and network analysis is reckoned to be a strong tool for describing their architecture. Many species assemblies show strong macroecological patterns, e.g. increasing species richness with decreasing latitude, but whether this latitudinal diversity gradient scales up to entities as complex as networks is unknown. We investigated this using a dataset of 54 community-wide pollination networks and hypothesized that pollination networks would display a latitudinal and altitudinal species richness gradient, increasing specialization towards the tropics, and that network topology would be affected by current climate. Location Global. Methods Each network was organized as a presence/absence matrix, consisting of P plant species, A pollinator species and their links. From these matrices, network parameters were estimated. Additionally, data about geography (latitude, elevation), climate at the network site (temperature, precipitation) and sampling effort (observation days) and extent (study-plot size) were gathered. Analyses were done using simultaneous autoregressive modelling (SAR). Results Species richness did not vary strongly with either latitude or elevation. However, network modularity decreased significantly with latitude whereas mean number of links per plant species (L p) and A/P ratio peaked at mid-latitude. Above 500 m a.s.l., A/P ratio decreased and mean number of links per pollinator species (L a) increased with elevation. L p displayed mid-ambient peaks with temperature and nestedness and modularity displayed linear relationships with precipitation. Main conclusion Pollination networks showed macroecological patterns. No strong latitudinal or altitudinal gradient in species richness was observed. L p and the A/P ratio peaked at mid-latitude whereas modularity decreased linearly. Both patterns are suggestive of a more specialized interaction structure towards the tropics. In particular, mean annual precipitation appeared influential on network topology as both nestedness and modularity varied significantly. Importantly, corrected regressions suggest that neither sampling effort nor extent affected the observed patterns.","accessed":{"date-parts":[["2025",3,24]]},"author":[{"family":"Trøjelsgaard","given":"Kristian"},{"family":"Olesen","given":"Jens M."}],"citation-key":"trojelsgaardMacroecologyPollinationNetworks2013","container-title":"Global Ecology and Biogeography","DOI":"10.1111/j.1466-8238.2012.00777.x","ISSN":"1466-8238","issue":"2","issued":{"date-parts":[["2013"]]},"language":"en","license":"© 2012 Blackwell Publishing Ltd","page":"149–162","source":"Wiley Online Library","title":"Macroecology of pollination networks","type":"article-journal","URL":"https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1466-8238.2012.00777.x","volume":"22"},{"id":"turnerTutorialApproximateBayesian2012","type":"article-journal","abstract":"This tutorial explains the foundation of approximate Bayesian computation (ABC), an approach to Bayesian inference that does not require the specification of a likelihood function, and hence that can be used to estimate posterior distributions of parameters for simulation-based models. We discuss briefly the philosophy of Bayesian inference and then present several algorithms for ABC. We then apply these algorithms in a number of examples. For most of these examples, the posterior distributions are known, and so we can compare the estimated posteriors derived from ABC to the true posteriors and verify that the algorithms recover the true posteriors accurately. We also consider a popular simulation-based model of recognition memory (REM) for which the true posteriors are unknown. We conclude with a number of recommendations for applying ABC methods to solve real-world problems.","citation-key":"turnerTutorialApproximateBayesian2012","container-title":"Journal of Mathematical Psychology","DOI":"10.1016/j.jmp.2012.02.005","ISSN":"0022-2496","issue":"2","journalAbbreviation":"Journal of Mathematical Psychology","page":"69-85","source":"ScienceDirect","title":"A tutorial on approximate Bayesian computation","URL":"https://www.sciencedirect.com/science/article/pii/S0022249612000272","volume":"56","author":[{"family":"Turner","given":"Brandon M."},{"family":"Van Zandt","given":"Trisha"}],"accessed":{"date-parts":[["2026",5,15]]},"issued":{"date-parts":[["2012",4,1]]},"library":"HappyR ABC","citekey":"turnerTutorialApproximateBayesian2012"},{"id":"Turochamp2023","abstract":"Turochamp est un programme d'échecs et le premier jeu développé pour un ordinateur, développé en 1948 par Alan Turing et D. G. Champernowne. Le duo écrit les algorithmes alors qu'il n'a pas d'ordinateur, puis Turing tente d'adapter le programme sur Ferranti Mark I, mais l'écriture reste inachevée. Le programme utilise notamment d'importantes méthodes d'évaluation et des concepts de sélectivité. Toutefois, son fonctionnement n'est pas basé sur une recherche exhaustive, mais plutôt sur une orientation de type heuristique. En 1952, un ami de Turing joue contre Turochamp et gagne la partie, alors que Turing simule à la main les calculs normalement pris en charge par l'ordinateur.\nÀ l'occasion des 100 ans de la naissance d'Alan Turing, en 2012, le programme est reconstruit par des experts informatiques, et Garry Kasparov, l'un des meilleurs joueurs de l'histoire du jeu d'échecs, joue une partie contre lui, qu'il gagne facilement tout en reconnaissant le contexte historique et la qualité de Turochamp.\nTurochamp reste le premier programme d'échecs, conçu avant même les premiers ordinateurs. Toutefois, en novembre 1951, Dietrich Prinz, qui a travaillé pour Ferranti, développe le premier programme d'échecs fonctionnant sur un ordinateur, le Ferranti Mark I du Massachusetts Institute of Technology (MIT).","accessed":{"date-parts":[["2024",2,11]]},"citation-key":"Turochamp2023","container-title":"Wikipédia","issued":{"date-parts":[["2023",8,28]]},"language":"fr","license":"Creative Commons Attribution-ShareAlike License","note":"Page Version ID: 207350903","source":"Wikipedia","title":"<i>Turochamp</i>","type":"entry-encyclopedia","URL":"https://fr.wikipedia.org/w/index.php?title=Turochamp&oldid=207350903"},{"id":"uncklessModelingManipulationNatural2015","abstract":"The use of recombinant genetic technologies for population manipulation has mostly remained an abstract idea due to the lack of a suitable means to drive novel gene constructs to high frequency in populations. Recently Gantz and Bier showed that the use of CRISPR/Cas9 technology could provide an artificial drive mechanism, the so-called mutagenic chain reaction (MCR), which could lead to rapid fixation of even a deleterious introduced allele. We establish the near equivalence of this system to other gene drive models and review the results of simple models showing that, when there is a fitness cost to the MCR allele, an internal equilibrium may exist that is usually unstable. In this case, introductions must be at a frequency above this critical point for the successful invasion of the MCR allele. We obtain estimates of fixation and invasion probabilities for the appropriate scenarios. Finally, we discuss how polymorphism in natural populations may introduce sources of natural resistance to MCR invasion. These modeling results have important implications for application of MCR in natural populations.","accessed":{"date-parts":[["2024",10,3]]},"author":[{"family":"Unckless","given":"Robert L"},{"family":"Messer","given":"Philipp W"},{"family":"Connallon","given":"Tim"},{"family":"Clark","given":"Andrew G"}],"citation-key":"uncklessModelingManipulationNatural2015","container-title":"Genetics","DOI":"10.1534/genetics.115.177592","ISSN":"1943-2631","issue":"2","issued":{"date-parts":[["2015",10,1]]},"language":"en","license":"https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model","page":"425–431","source":"DOI.org (Crossref)","title":"Modeling the Manipulation of Natural Populations by the Mutagenic Chain Reaction","type":"article-journal","URL":"https://academic.oup.com/genetics/article/201/2/425/5930039","volume":"201"},{"id":"vartyAlphaZeroMonte","abstract":"Description of the post","accessed":{"date-parts":[["2024",2,8]]},"author":[{"family":"Varty","given":"Josh"}],"citation-key":"vartyAlphaZeroMonte","language":"en","title":"Alpha Zero And Monte Carlo Tree Search","type":"webpage","URL":"https://joshvarty.github.io/AlphaZero/"},{"id":"velickovicGraphAttentionNetworks2018","abstract":"We present graph attention networks (GATs), novel neural network architectures that operate on graph-structured data, leveraging masked self-attentional layers to address the shortcomings of prior methods based on graph convolutions or their approximations. By stacking layers in which nodes are able to attend over their neighborhoods’ features, we enable (implicitly) specifying different weights to different nodes in a neighborhood, without requiring any kind of costly matrix operation (such as inversion) or depending on knowing the graph structure upfront. In this way, we address several key challenges of spectral-based graph neural networks simultaneously, and make our model readily applicable to inductive as well as transductive problems. Our GAT models have achieved or matched state-of-theart results across four established transductive and inductive graph benchmarks: the Cora, Citeseer and Pubmed citation network datasets, as well as a proteinprotein interaction dataset (wherein test graphs remain unseen during training).","accessed":{"date-parts":[["2024",5,14]]},"author":[{"family":"Veličković","given":"Petar"},{"family":"Cucurull","given":"Guillem"},{"family":"Casanova","given":"Arantxa"},{"family":"Romero","given":"Adriana"},{"family":"Liò","given":"Pietro"},{"family":"Bengio","given":"Yoshua"}],"citation-key":"velickovicGraphAttentionNetworks2018","issued":{"date-parts":[["2018",2,4]]},"language":"en","number":"arXiv:1710.10903","publisher":"arXiv","source":"arXiv.org","title":"Graph Attention Networks","type":"article","URL":"http://arxiv.org/abs/1710.10903"},{"id":"vinyalsGrandmasterLevelStarCraft2019","abstract":"Many real-world applications require artificial agents to compete and coordinate with other agents in complex environments. As a stepping stone to this goal, the domain of StarCraft has emerged as an important challenge for artificial intelligence research, owing to its iconic and enduring status among the most difficult professional esports and its relevance to the real world in terms of its raw complexity and multi-agent challenges. Over the course of a decade and numerous competitions1–3, the strongest agents have simplified important aspects of the game, utilized superhuman capabilities, or employed hand-crafted sub-systems4. Despite these advantages, no previous agent has come close to matching the overall skill of top StarCraft players. We chose to address the challenge of StarCraft using general-purpose learning methods that are in principle applicable to other complex domains: a multi-agent reinforcement learning algorithm that uses data from both human and agent games within a diverse league of continually adapting strategies and counter-strategies, each represented by deep neural networks5,6. We evaluated our agent, AlphaStar, in the full game of StarCraft II, through a series of online games against human players. AlphaStar was rated at Grandmaster level for all three StarCraft races and above 99.8% of officially ranked human players.","accessed":{"date-parts":[["2024",2,5]]},"author":[{"family":"Vinyals","given":"Oriol"},{"family":"Babuschkin","given":"Igor"},{"family":"Czarnecki","given":"Wojciech M."},{"family":"Mathieu","given":"Michaël"},{"family":"Dudzik","given":"Andrew"},{"family":"Chung","given":"Junyoung"},{"family":"Choi","given":"David H."},{"family":"Powell","given":"Richard"},{"family":"Ewalds","given":"Timo"},{"family":"Georgiev","given":"Petko"},{"family":"Oh","given":"Junhyuk"},{"family":"Horgan","given":"Dan"},{"family":"Kroiss","given":"Manuel"},{"family":"Danihelka","given":"Ivo"},{"family":"Huang","given":"Aja"},{"family":"Sifre","given":"Laurent"},{"family":"Cai","given":"Trevor"},{"family":"Agapiou","given":"John P."},{"family":"Jaderberg","given":"Max"},{"family":"Vezhnevets","given":"Alexander S."},{"family":"Leblond","given":"Rémi"},{"family":"Pohlen","given":"Tobias"},{"family":"Dalibard","given":"Valentin"},{"family":"Budden","given":"David"},{"family":"Sulsky","given":"Yury"},{"family":"Molloy","given":"James"},{"family":"Paine","given":"Tom L."},{"family":"Gulcehre","given":"Caglar"},{"family":"Wang","given":"Ziyu"},{"family":"Pfaff","given":"Tobias"},{"family":"Wu","given":"Yuhuai"},{"family":"Ring","given":"Roman"},{"family":"Yogatama","given":"Dani"},{"family":"Wünsch","given":"Dario"},{"family":"McKinney","given":"Katrina"},{"family":"Smith","given":"Oliver"},{"family":"Schaul","given":"Tom"},{"family":"Lillicrap","given":"Timothy"},{"family":"Kavukcuoglu","given":"Koray"},{"family":"Hassabis","given":"Demis"},{"family":"Apps","given":"Chris"},{"family":"Silver","given":"David"}],"citation-key":"vinyalsGrandmasterLevelStarCraft2019","container-title":"Nature","DOI":"10.1038/s41586-019-1724-z","ISSN":"1476-4687","issue":"7782","issued":{"date-parts":[["2019",11]]},"language":"en","license":"2019 The Author(s), under exclusive licence to Springer Nature Limited","number":"7782","page":"350–354","publisher":"Nature Publishing Group","source":"www.nature.com","title":"Grandmaster level in StarCraft II using multi-agent reinforcement learning","type":"article-journal","URL":"https://www.nature.com/articles/s41586-019-1724-z","volume":"575"},{"id":"vishwanathanGraphKernels2010","abstract":"We present a unified framework to study graph kernels, special cases of which include the random walk (Gärtner et al., 2003; Borgwardt et al., 2005) and marginalized (Kashima et al., 2003, 2004; Mahét al., 2004) graph kernels. Through reduction to a Sylvester equation we improve the time complexity of kernel computation between unlabeled graphs with n vertices from O(n6) to O(n3). We find a spectral decomposition approach even more efficient when computing entire kernel matrices. For labeled graphs we develop conjugate gradient and fixed-point methods that take O(dn3) time per iteration, where d is the size of the label set. By extending the necessary linear algebra to Reproducing Kernel Hilbert Spaces (RKHS) we obtain the same result for d-dimensional edge kernels, and O(n4) in the infinite-dimensional case; on sparse graphs these algorithms only take O(n2) time per iteration in all cases. Experiments on graphs from bioinformatics and other application domains show that these techniques can speed up computation of the kernel by an order of magnitude or more. We also show that certain rational kernels (Cortes et al., 2002, 2003, 2004) when specialized to graphs reduce to our random walk graph kernel. Finally, we relate our framework to R-convolution kernels (Haussler, 1999) and provide a kernel that is close to the optimal assignment kernel of kernel of Fröhlich et al. (2006) yet provably positive semi-definite.","author":[{"family":"Vishwanathan","given":"S. V. N."},{"family":"Schraudolph","given":"Nicol N."},{"family":"Kondor","given":"Risi"},{"family":"Borgwardt","given":"Karsten M."}],"citation-key":"vishwanathanGraphKernels2010","container-title":"J. Mach. Learn. Res.","ISSN":"1532-4435","issued":{"date-parts":[["2010",8,1]]},"page":"1201–1242","source":"ACM Digital Library","title":"Graph Kernels","type":"article-journal","volume":"11"},{"id":"wangAlphaGOLADZeroMastering","abstract":"Monte Carlo Tree Search, neural network-based policy and value evaluation, and self-play are three popular techniques widely used in reinforcement learning. Inspired by the recent AlphaGo Zero paper, we design, construct, train, and evaluate an agent to play the Game of Life and Death (GOLAD) using a combination of the aforementioned techniques. GOLAD is a two-player game based on Conway’s Game of Life (GOL), where players can manipulate their cells after each simulation step. We obtain positive results on a small board versus a random agent, but challenges remain in transferring our player to larger boards and playing versus more sophisticated opponents.","author":[{"family":"Wang","given":"Elias"},{"family":"Lee","given":"Hana"},{"family":"Jiang","given":"Zhilin"}],"citation-key":"wangAlphaGOLADZeroMastering","language":"en","source":"Zotero","title":"AlphaGOLAD Zero: Mastering the Game of Life and Death with Self-Play","type":"article-journal"},{"id":"wangAmortizedProbabilisticDetection2024","abstract":"Learning community structures in graphs has broad applications across scientific domains. While graph neural networks (GNNs) have been successful in encoding graph structures, existing GNN-based methods for community detection are limited by requiring knowledge of the number of communities in advance, in addition to lacking a proper probabilistic formulation to handle uncertainty. We propose a simple framework for amortized community detection, which addresses both of these issues by combining the expressive power of GNNs with recent methods for amortized clustering. Our models consist of a graph representation backbone that extracts structural information and an amortized clustering network that naturally handles variable numbers of clusters. Both components combine into well-defined models of the posterior distribution of graph communities and are jointly optimized given labeled graphs. At inference time, the models yield parallel samples from the posterior of community labels, quantifying uncertainty in a principled way. We evaluate several models from our framework on synthetic and real datasets, and demonstrate improved performance compared to previous methods. As a separate contribution, we extend recent amortized probabilistic clustering architectures by adding attention modules, which yield further improvements on community detection tasks.","accessed":{"date-parts":[["2026",4,16]]},"author":[{"family":"Wang","given":"Yueqi"},{"family":"Lee","given":"Yoonho"},{"family":"Basu","given":"Pallab"},{"family":"Lee","given":"Juho"},{"family":"Teh","given":"Yee Whye"},{"family":"Paninski","given":"Liam"},{"family":"Pakman","given":"Ari"}],"citation-key":"wangAmortizedProbabilisticDetection2024","DOI":"10.48550/arXiv.2010.15727","issued":{"date-parts":[["2024",8,2]]},"note":"Read_Status: New\nRead_Status_Date: 2026-04-16T12:33:33.999Z","number":"arXiv:2010.15727","source":"arXiv.org","title":"Amortized Probabilistic Detection of Communities in Graphs","type":"article","URL":"http://arxiv.org/abs/2010.15727"},{"id":"wangNeuralEntropicGromovWasserstein2023","abstract":"The Gromov-Wasserstein (GW) distance, rooted in optimal transport (OT) theory, provides a natural framework for aligning heterogeneous datasets. Alas, statistical estimation of the GW distance suffers from the curse of dimensionality and its exact computation is NP hard. To circumvent these issues, entropic regularization has emerged as a remedy that enables parametric estimation rates via plug-in and efficient computation using Sinkhorn iterations. Motivated by further scaling up entropic GW (EGW) alignment methods to data dimensions and sample sizes that appear in modern machine learning applications, we propose a novel neural estimation approach. Our estimator parametrizes a minimax semi-dual representation of the EGW distance by a neural network, approximates expectations by sample means, and optimizes the resulting empirical objective over parameter space. We establish non-asymptotic error bounds on the EGW neural estimator of the alignment cost and optimal plan. Our bounds characterize the effective error in terms of neural network (NN) size and the number of samples, revealing optimal scaling laws that guarantee parametric convergence. The bounds hold for compactly supported distributions, and imply that the proposed estimator is minimax-rate optimal over that class. Numerical experiments validating our theory are also provided.","accessed":{"date-parts":[["2025",6,11]]},"author":[{"family":"Wang","given":"Tao"},{"family":"Goldfeld","given":"Ziv"}],"citation-key":"wangNeuralEntropicGromovWasserstein2023","DOI":"10.48550/arXiv.2312.07397","issued":{"date-parts":[["2023",12,12]]},"note":"Read_Status: New\nRead_Status_Date: 2025-06-11T15:49:30.151Z","number":"arXiv:2312.07397","publisher":"arXiv","source":"arXiv.org","title":"Neural Entropic Gromov-Wasserstein Alignment","type":"article","URL":"http://arxiv.org/abs/2312.07397"},{"id":"wassermanAllStatisticsConcise2004","accessed":{"date-parts":[["2025",12,22]]},"author":[{"family":"Wasserman","given":"Larry"}],"citation-key":"wassermanAllStatisticsConcise2004","collection-title":"Springer Texts in Statistics","DOI":"10.1007/978-0-387-21736-9","ISBN":"978-1-4419-2322-6 978-0-387-21736-9","issued":{"date-parts":[["2004"]]},"language":"en","license":"http://www.springer.com/tdm","note":"Read_Status: New\nRead_Status_Date: 2025-12-22T09:35:57.494Z","publisher":"Springer New York","publisher-place":"New York, NY","source":"DOI.org (Crossref)","title":"All of Statistics: A Concise Course in Statistical Inference","title-short":"All of Statistics","type":"book","URL":"http://link.springer.com/10.1007/978-0-387-21736-9"},{"id":"wassermanThisPagePrinter","author":[{"family":"Wasserman","given":"Larry"}],"citation-key":"wassermanThisPagePrinter","language":"en","note":"Read_Status: New\nRead_Status_Date: 2025-12-22T09:27:38.221Z","source":"Zotero","title":"This is page i Printer: Opaque this","type":"article-journal"},{"id":"WebLifeEcological2022","accessed":{"date-parts":[["2023",6,17]]},"citation-key":"WebLifeEcological2022","issued":{"date-parts":[["2022",7]]},"title":"Web of Life: ecological networks database","type":"webpage","URL":"https://www.web-of-life.es/map.php"},{"id":"WebLifeEcological2022","accessed":{"date-parts":[["2023",6,17]]},"citation-key":"WebLifeEcological2022","issued":{"date-parts":[["2022",7]]},"title":"Web of Life: Ecological networks database","type":"webpage","URL":"https://www.web-of-life.es/map.php"},{"id":"WideCrossspeciesRNASeq","accessed":{"date-parts":[["2023",11,20]]},"citation-key":"WideCrossspeciesRNASeq","title":"Wide cross‐species RNA‐Seq comparison reveals convergent molecular mechanisms involved in nickel hyperaccumulation across dicotyledons - García de la Torre - 2021 - New Phytologist - Wiley Online Library","type":"webpage","URL":"https://nph.onlinelibrary.wiley.com/doi/full/10.1111/nph.16775"},{"id":"williamsSimplextoEuclideanBijectionConjugate2026","abstract":"We propose a conjugate and calibrated Gaussian process (GP) model for multi-class classification by exploiting the geometry of the probability simplex. Our approach uses Aitchison geometry to map simplex-valued class probabilities to an unconstrained Euclidean representation, turning classification into a GP regression problem with fewer latent dimensions than standard multi-class GP classifiers. This yields conjugate inference and reliable predictive probabilities without relying on distributional approximations in the model construction. The method is compatible with standard sparse GP regression techniques, enabling scalable inference on larger datasets. Empirical results show well-calibrated and competitive performance across synthetic and real-world datasets.","accessed":{"date-parts":[["2026",4,12]]},"author":[{"family":"Williams","given":"Bernardo"},{"family":"Tetali","given":"Harsha Vardhan"},{"family":"Klami","given":"Arto"},{"family":"Hartmann","given":"Marcelo"}],"citation-key":"williamsSimplextoEuclideanBijectionConjugate2026","DOI":"10.48550/arXiv.2603.16621","issued":{"date-parts":[["2026",3,17]]},"language":"en","note":"Read_Status: New\nRead_Status_Date: 2026-04-12T17:33:08.131Z","number":"arXiv:2603.16621","publisher":"arXiv","source":"arXiv.org","title":"Simplex-to-Euclidean Bijection for Conjugate and Calibrated Multiclass Gaussian Process","type":"article","URL":"http://arxiv.org/abs/2603.16621"},{"id":"willsMetricsGraphComparison2020","abstract":"Comparison of graph structure is a ubiquitous task in data analysis and machine learning, with diverse applications in fields such as neuroscience, cyber security, social network analysis, and bioinformatics, among others. Discovery and comparison of structures such as modular communities, rich clubs, hubs, and trees yield insight into the generative mechanisms and functional properties of the graph. Often, two graphs are compared via a pairwise distance measure, with a small distance indicating structural similarity and vice versa. Common choices include spectral distances and distances based on node affinities. However, there has of yet been no comparative study of the efficacy of these distance measures in discerning between common graph topologies at different structural scales. In this work, we compare commonly used graph metrics and distance measures, and demonstrate their ability to discern between common topological features found in both random graph models and real world networks. We put forward a multi-scale picture of graph structure wherein we study the effect of global and local structures on changes in distance measures. We make recommendations on the applicability of different distance measures to the analysis of empirical graph data based on this multi-scale view. Finally, we introduce the Python library NetComp that implements the graph distances used in this work.","accessed":{"date-parts":[["2025",5,20]]},"author":[{"family":"Wills","given":"Peter"},{"family":"Meyer","given":"François G."}],"citation-key":"willsMetricsGraphComparison2020","container-title":"PLOS ONE","container-title-short":"PLOS ONE","DOI":"10.1371/journal.pone.0228728","ISSN":"1932-6203","issue":"2","issued":{"date-parts":[["2020",2,12]]},"language":"en","note":"Read_Status: New\nRead_Status_Date: 2025-05-20T12:22:29.607Z","page":"e0228728","publisher":"Public Library of Science","source":"PLoS Journals","title":"Metrics for graph comparison: A practitioner’s guide","title-short":"Metrics for graph comparison","type":"article-journal","URL":"https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0228728","volume":"15"},{"id":"willsMetricsGraphComparison2020a","abstract":"Comparison of graph structure is a ubiquitous task in data analysis and machine learning, with diverse applications in fields such as neuroscience, cyber security, social network analysis, and bioinformatics, among others. Discovery and comparison of structures such as modular communities, rich clubs, hubs, and trees yield insight into the generative mechanisms and functional properties of the graph. Often, two graphs are compared via a pairwise distance measure, with a small distance indicating structural similarity and vice versa. Common choices include spectral distances and distances based on node affinities. However, there has of yet been no comparative study of the efficacy of these distance measures in discerning between common graph topologies at different structural scales. In this work, we compare commonly used graph metrics and distance measures, and demonstrate their ability to discern between common topological features found in both random graph models and real world networks. We put forward a multi-scale picture of graph structure wherein we study the effect of global and local structures on changes in distance measures. We make recommendations on the applicability of different distance measures to the analysis of empirical graph data based on this multi-scale view. Finally, we introduce the Python library NetComp that implements the graph distances used in this work.","accessed":{"date-parts":[["2025",9,18]]},"author":[{"family":"Wills","given":"Peter"},{"family":"Meyer","given":"François G."}],"citation-key":"willsMetricsGraphComparison2020a","container-title":"PLOS ONE","DOI":"10.1371/journal.pone.0228728","ISSN":"1932-6203","issue":"2","issued":{"date-parts":[["2020",2,12]]},"language":"english","page":"e0228728","publisher":"Public Library of Science","title":"Metrics for graph comparison: A practitioner’s guide","title-short":"Metrics for graph comparison","type":"article-journal","URL":"https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0228728","volume":"15"},{"id":"willsMetricsGraphComparison2020b","abstract":"Comparison of graph structure is a ubiquitous task in data analysis and machine learning, with diverse applications in fields such as neuroscience, cyber security, social network analysis, and bioinformatics, among others. Discovery and comparison of structures such as modular communities, rich clubs, hubs, and trees yield insight into the generative mechanisms and functional properties of the graph. Often, two graphs are compared via a pairwise distance measure, with a small distance indicating structural similarity and vice versa. Common choices include spectral distances and distances based on node affinities. However, there has of yet been no comparative study of the efficacy of these distance measures in discerning between common graph topologies at different structural scales. In this work, we compare commonly used graph metrics and distance measures, and demonstrate their ability to discern between common topological features found in both random graph models and real world networks. We put forward a multi-scale picture of graph structure wherein we study the effect of global and local structures on changes in distance measures. We make recommendations on the applicability of different distance measures to the analysis of empirical graph data based on this multi-scale view. Finally, we introduce the Python library NetComp that implements the graph distances used in this work.","accessed":{"date-parts":[["2025",9,18]]},"author":[{"family":"Wills","given":"Peter"},{"family":"Meyer","given":"François G."}],"citation-key":"willsMetricsGraphComparison2020b","container-title":"PLOS ONE","container-title-short":"PLOS ONE","DOI":"10.1371/journal.pone.0228728","ISSN":"1932-6203","issue":"2","issued":{"date-parts":[["2020",2,12]]},"language":"en","note":"Read_Status: New\nRead_Status_Date: 2025-09-18T14:47:55.289Z","page":"e0228728","publisher":"Public Library of Science","source":"PLoS Journals","title":"Metrics for graph comparison: A practitioner’s guide","title-short":"Metrics for graph comparison","type":"article-journal","URL":"https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0228728","volume":"15"},{"id":"wilmsTreebasedNodeAggregation2022","abstract":"High-dimensional graphical models are often estimated using regularization that is aimed at reducing the number of edges in a network. In this work, we show how even simpler networks can be produced by aggregating the nodes of the graphical model. We develop a new convex regularized method, called the tree-aggregated graphical lasso or tag-lasso, that estimates graphical models that are both edge-sparse and node-aggregated. The aggregation is performed in a data-driven fashion by leveraging side information in the form of a tree that encodes node similarity and facilitates the interpretation of the resulting aggregated nodes. We provide an efficient implementation of the tag-lasso by using the locally adaptive alternating direction method of multipliers and illustrate our proposal's practical advantages in simulation and in applications in finance and biology.","accessed":{"date-parts":[["2025",10,14]]},"author":[{"family":"Wilms","given":"Ines"},{"family":"Bien","given":"Jacob"}],"citation-key":"wilmsTreebasedNodeAggregation2022","container-title":"Journal of Machine Learning Research","ISSN":"1533-7928","issue":"243","issued":{"date-parts":[["2022"]]},"note":"Read_Status: New\nRead_Status_Date: 2025-10-14T12:07:34.769Z","page":"1–36","source":"www.jmlr.org","title":"Tree-based Node Aggregation in Sparse Graphical Models","type":"article-journal","URL":"http://jmlr.org/papers/v23/21-0105.html","volume":"23"},{"id":"wittes331NoteBias1972","abstract":"This note demonstrates that Chapman's estimate NU for population size in a two-sample capture-recapture experiment is unbiased when the sum of the sample sizes is at least as great as the population size. Further, an estimate of the variance of NU is proposed and is shown to be unbiased when n1 + n2 ≥ N.","accessed":{"date-parts":[["2025",1,16]]},"author":[{"family":"Wittes","given":"Janet T."}],"citation-key":"wittes331NoteBias1972","container-title":"Biometrics","DOI":"10.2307/2556173","ISSN":"0006-341X","issue":"2","issued":{"date-parts":[["1972"]]},"page":"592–597","publisher":"[Wiley, International Biometric Society]","source":"JSTOR","title":"331. Note: On the Bias and Estimated Variance of Chapman's Two-Sample Capture-Recapture Population Estimate","title-short":"331. Note","type":"article-journal","URL":"https://www.jstor.org/stable/2556173","volume":"28"},{"id":"xuHowPowerfulAre2019","abstract":"Graph Neural Networks (GNNs) are an effective framework for representation learning of graphs. GNNs follow a neighborhood aggregation scheme, where the representation vector of a node is computed by recursively aggregating and transforming representation vectors of its neighboring nodes. Many GNN variants have been proposed and have achieved state-of-the-art results on both node and graph classification tasks. However, despite GNNs revolutionizing graph representation learning, there is limited understanding of their representational properties and limitations. Here, we present a theoretical framework for analyzing the expressive power of GNNs to capture different graph structures. Our results characterize the discriminative power of popular GNN variants, such as Graph Convolutional Networks and GraphSAGE, and show that they cannot learn to distinguish certain simple graph structures. We then develop a simple architecture that is provably the most expressive among the class of GNNs and is as powerful as the Weisfeiler-Lehman graph isomorphism test. We empirically validate our theoretical findings on a number of graph classification benchmarks, and demonstrate that our model achieves state-of-the-art performance.","accessed":{"date-parts":[["2024",5,14]]},"author":[{"family":"Xu","given":"Keyulu"},{"family":"Hu","given":"Weihua"},{"family":"Leskovec","given":"Jure"},{"family":"Jegelka","given":"Stefanie"}],"citation-key":"xuHowPowerfulAre2019","DOI":"10.48550/arXiv.1810.00826","issued":{"date-parts":[["2019",2,22]]},"number":"arXiv:1810.00826","publisher":"arXiv","source":"arXiv.org","title":"How Powerful are Graph Neural Networks?","type":"article","URL":"http://arxiv.org/abs/1810.00826"},{"id":"xuUnderstandingGraphEmbedding2021","abstract":"Graph analytics can lead to better quantitative understanding and control of complex networks, but traditional methods suffer from the high computational cost and excessive memory requirements associated with the high-dimensionality and heterogeneous characteristics of industrial size networks. Graph embedding techniques can be effective in converting high-dimensional sparse graphs into low-dimensional, dense, and continuous vector spaces, preserving maximally the graph structure properties. Another type of emerging graph embedding employs Gaussian distribution--based graph embedding with important uncertainty estimation. The main goal of graph embedding methods is to pack every node's properties into a vector with a smaller dimension; hence, node similarity in the original complex irregular spaces can be easily quantified in the embedded vector spaces using standard metrics. The nonlinear and highly informative graph embeddings generated in the latent space can be conveniently used to address different downstream graph analytics tasks (e.g., node classification, link prediction, community detection, visualization, etc.). In this review, we present some fundamental concepts in graph analytics and graph embedding methods, focusing in particular on random walk--based and neural network--based methods. We also discuss the emerging deep learning--based dynamic graph embedding methods. We highlight the distinct advantages of graph embedding methods in four diverse applications, and we present implementation details and references to open-source software as well as available databases in the supplementary material to help interested readers start their exploration into graph analytics.","accessed":{"date-parts":[["2025",9,19]]},"author":[{"family":"Xu","given":"Mengjia"}],"citation-key":"xuUnderstandingGraphEmbedding2021","container-title":"SIAM Review","container-title-short":"SIAM Rev.","DOI":"10.1137/20M1386062","ISSN":"0036-1445, 1095-7200","issue":"4","issued":{"date-parts":[["2021",1]]},"language":"en","note":"Read_Status: New\nRead_Status_Date: 2025-09-19T12:36:55.370Z","page":"825–853","source":"DOI.org (Crossref)","title":"Understanding Graph Embedding Methods and Their Applications","type":"article-journal","URL":"https://epubs.siam.org/doi/10.1137/20M1386062","volume":"63"},{"id":"xuUnderstandingGraphEmbedding2021","abstract":"Graph analytics can lead to better quantitative understanding and control of complex networks, but traditional methods suffer from the high computational cost and excessive memory requirements associated with the high-dimensionality and heterogeneous characteristics of industrial size networks. Graph embedding techniques can be effective in converting high-dimensional sparse graphs into low-dimensional, dense, and continuous vector spaces, preserving maximally the graph structure properties. Another type of emerging graph embedding employs Gaussian distribution–based graph embedding with important uncertainty estimation. The main goal of graph embedding methods is to pack every node's properties into a vector with a smaller dimension; hence, node similarity in the original complex irregular spaces can be easily quantified in the embedded vector spaces using standard metrics. The nonlinear and highly informative graph embeddings generated in the latent space can be conveniently used to address different downstream graph analytics tasks (e.g., node classification, link prediction, community detection, visualization, etc.). In this review, we present some fundamental concepts in graph analytics and graph embedding methods, focusing in particular on random walk–based and neural network–based methods. We also discuss the emerging deep learning–based dynamic graph embedding methods. We highlight the distinct advantages of graph embedding methods in four diverse applications, and we present implementation details and references to open-source software as well as available databases in the supplementary material to help interested readers start their exploration into graph analytics.","accessed":{"date-parts":[["2025",9,19]]},"author":[{"family":"Xu","given":"Mengjia"}],"citation-key":"xuUnderstandingGraphEmbedding2021","container-title":"Siam Review","container-title-short":"SIAM Rev.","DOI":"10.1137/20M1386062","ISSN":"0036-1445, 1095-7200","issue":"4","issued":{"date-parts":[["2021",1]]},"language":"english","page":"825–853","title":"Understanding Graph Embedding Methods and Their Applications","type":"article-journal","URL":"https://epubs.siam.org/doi/10.1137/20M1386062","volume":"63"},{"id":"yangDeepLatentSpace2024","abstract":"Graph representation learning is a fundamental problem for modeling relational data and benefits a number of downstream applications. Traditional Bayesian-based graph models and recent deep learning based GNN either suffer from impracticability or lack interpretability, thus combined models for undirected graphs have been proposed to overcome the weaknesses. As a large portion of real-world graphs are directed graphs (of which undirected graphs are special cases), in this paper, we propose a Deep Latent Space Model (DLSM) for directed graphs to incorporate the traditional latent variable based generative model into deep learning frameworks. Our proposed model consists of a graph convolutional network (GCN) encoder and a stochastic decoder, which are layer-wise connected by a hierarchical variational auto-encoder architecture. By specifically modeling the degree heterogeneity using node random factors, our model possesses better interpretability in both community structure and degree heterogeneity. For fast inference, the stochastic gradient variational Bayes (SGVB) is adopted using a non-iterative recognition model, which is much more scalable than traditional MCMC-based methods. The experiments on real-world datasets show that the proposed model achieves the state-of-the-art performances on both link prediction and community detection tasks while learning interpretable node embeddings. The source code is available at https://github.com/upperr/DLSM.","accessed":{"date-parts":[["2024",5,20]]},"author":[{"family":"Yang","given":"Hanxuan"},{"family":"Kong","given":"Qingchao"},{"family":"Mao","given":"Wenji"}],"citation-key":"yangDeepLatentSpace2024","container-title":"Neurocomputing","container-title-short":"Neurocomputing","DOI":"10.1016/j.neucom.2024.127342","ISSN":"09252312","issued":{"date-parts":[["2024",4]]},"language":"en","page":"127342","source":"arXiv.org","title":"A Deep Latent Space Model for Graph Representation Learning","type":"article-journal","URL":"http://arxiv.org/abs/2106.11721","volume":"576"},{"id":"yumpu.comInsectPollinatorsMer","abstract":"Insect pollinators of the Mer Bleue peat bog of Ottawa - Biodiversity ...","accessed":{"date-parts":[["2023",8,6]]},"author":[{"family":"Yumpu.com","given":""}],"citation-key":"yumpu.comInsectPollinatorsMer","container-title":"yumpu.com","language":"en","title":"Insect pollinators of the Mer Bleue peat bog of Ottawa - Biodiversity ...","type":"webpage","URL":"https://www.yumpu.com/en/document/view/11762821/insect-pollinators-of-the-mer-bleue-peat-bog-of-ottawa-biodiversity-"},{"id":"yurdemFederatedLearningOverview2024","accessed":{"date-parts":[["2026",4,1]]},"author":[{"family":"Yurdem","given":"Betul"},{"family":"Kuzlu","given":"Murat"},{"family":"Gullu","given":"Mehmet Kemal"},{"family":"Catak","given":"Ferhat Ozgur"},{"family":"Tabassum","given":"Maliha"}],"citation-key":"yurdemFederatedLearningOverview2024","container-title":"Heliyon","container-title-short":"Heliyon","DOI":"10.1016/j.heliyon.2024.e38137","ISSN":"24058440","issue":"19","issued":{"date-parts":[["2024",10]]},"language":"en","note":"Read_Status: New\nRead_Status_Date: 2026-04-01T09:10:19.764Z","page":"e38137","source":"DOI.org (Crossref)","title":"Federated learning: Overview, strategies, applications, tools and future directions","title-short":"Federated learning","type":"article-journal","URL":"https://linkinghub.elsevier.com/retrieve/pii/S2405844024141680","volume":"10"},{"id":"zhangPrimaldualFixedPoint","author":[{"family":"Zhang","given":"Xiaoqun"}],"citation-key":"zhangPrimaldualFixedPoint","language":"en","note":"Read_Status: New\nRead_Status_Date: 2026-01-12T13:42:24.356Z","source":"Zotero","title":"Primal-dual fixed point algorithms for separable minimization problems and their applications in imaging","type":"article-journal"},{"id":"zhouPredictingMissingLinks2009","abstract":"Missing link prediction in networks is of both theoretical interest and practical significance in modern science. In this paper, we empirically investigate a simple framework of link prediction on the basis of node similarity. We compare nine well-known local similarity measures on six real networks. The results indicate that the simplest measure, namely Common Neighbours, has the best overall performance, and the Adamic-Adar index performs second best. A new similarity measure, motivated by the resource allocation process taking place on networks, is proposed and shown to have higher prediction accuracy than common neighbours. It is found that many links are assigned the same scores if only the information of the nearest neighbours is used. We therefore design another new measure exploiting information on the next nearest neighbours, which can remarkably enhance the prediction accuracy.","accessed":{"date-parts":[["2025",4,11]]},"author":[{"family":"Zhou","given":"Tao"},{"family":"Lü","given":"Linyuan"},{"family":"Zhang","given":"Yi-Cheng"}],"citation-key":"zhouPredictingMissingLinks2009","container-title":"The European Physical Journal B","container-title-short":"Eur. Phys. J. B","DOI":"10.1140/epjb/e2009-00335-8","ISSN":"1434-6028, 1434-6036","issue":"4","issued":{"date-parts":[["2009",10]]},"language":"en","license":"http://www.springer.com/tdm","page":"623–630","source":"DOI.org (Crossref)","title":"Predicting missing links via local information","type":"article-journal","URL":"http://link.springer.com/10.1140/epjb/e2009-00335-8","volume":"71"},{"id":"zhouPredictingMissingLinks2009","abstract":"Missing link prediction in networks is of both theoretical interest and practical significance in modern science. In this paper, we empirically investigate a simple framework of link prediction on the basis of node similarity. We compare nine well-known local similarity measures on six real networks. The results indicate that the simplest measure, namely Common Neighbours, has the best overall performance, and the Adamic-Adar index performs second best. A new similarity measure, motivated by the resource allocation process taking place on networks, is proposed and shown to have higher prediction accuracy than common neighbours. It is found that many links are assigned the same scores if only the information of the nearest neighbours is used. We therefore design another new measure exploiting information on the next nearest neighbours, which can remarkably enhance the prediction accuracy.","accessed":{"date-parts":[["2025",4,11]]},"author":[{"family":"Zhou","given":"Tao"},{"family":"Lü","given":"Linyuan"},{"family":"Zhang","given":"Yi-Cheng"}],"citation-key":"zhouPredictingMissingLinks2009","container-title":"The European Physical Journal B: Condensed Matter and Complex Systems","container-title-short":"Eur. Phys. J. B","DOI":"10.1140/epjb/e2009-00335-8","ISSN":"1434-6028, 1434-6036","issue":"4","issued":{"date-parts":[["2009",10]]},"language":"english","page":"623–630","title":"Predicting missing links via local information","type":"article-journal","URL":"http://link.springer.com/10.1140/epjb/e2009-00335-8","volume":"71"},{"id":"zhouPredictingMissingLinks2009a","abstract":"Missing link prediction in networks is of both theoreticalinterest and practical significance in modern science. In thispaper, we empirically investigate a simple framework of linkprediction on the basis of node similarity. We compare ninewell-known local similarity measures on six real networks. Theresults indicate that the simplest measure, namely CommonNeighbours, has the best overall performance, and the Adamic-Adarindex performs second best. A new similarity measure, motivated bythe resource allocation process taking place on networks, isproposed and shown to have higher prediction accuracy than commonneighbours. It is found that many links are assigned the same scoresif only the information of the nearest neighbours is used. Wetherefore design another new measure exploiting information on thenext nearest neighbours, which can remarkably enhance the predictionaccuracy.","accessed":{"date-parts":[["2025",4,11]]},"author":[{"family":"Zhou","given":"Tao"},{"family":"Lü","given":"Linyuan"},{"family":"Zhang","given":"Yi-Cheng"}],"citation-key":"zhouPredictingMissingLinks2009a","container-title":"The European Physical Journal B","container-title-short":"Eur. Phys. J. B","DOI":"10.1140/epjb/e2009-00335-8","ISSN":"1434-6036","issue":"4","issued":{"date-parts":[["2009",10,1]]},"language":"en","page":"623–630","source":"Springer Link","title":"Predicting missing links via local information","type":"article-journal","URL":"https://doi.org/10.1140/epjb/e2009-00335-8","volume":"71"},{"id":"zhouStochasticVariationalMethods2022","abstract":"Recent years have seen substantial advances in the development of biofunctional materials using synthetic polymers. The growing problem of elusive sequence-functionality relations for most biomaterials has driven researchers to seek more effective tools and analysis methods. In this study, statistical models are used to study sequence features of the recently reported random heteropolymers (RHP), which transport protons across lipid bilayers selectively and rapidly like natural proton channels. We utilized the probabilistic graphical model framework and developed a generalized hidden semi-Markov model (GHSMM-RHP) to extract the function-determining sequence features, including the transmembrane segments within a chain and the sequence heterogeneity among different chains. We developed stochastic variational methods for efficient inference on parameter estimation and predictions, and empirically studied their computational performance from a comparative perspective on Bayesian (i.e., stochastic variational Bayes) versus frequentist (i.e., stochastic variational expectation-maximization) frameworks that have been studied separately before. The real data results agree well with the laboratory experiments, and suggest GHSMM-RHP’s potential in predicting protein-like behavior at the polymer-chain level.","accessed":{"date-parts":[["2025",12,19]]},"author":[{"family":"Zhou","given":"Yun"},{"family":"Gong","given":"Boying"},{"family":"Jiang","given":"Tao"},{"family":"Xu","given":"Ting"},{"family":"Huang","given":"Haiyan"}],"citation-key":"zhouStochasticVariationalMethods2022","DOI":"10.48550/arXiv.2207.01813","issued":{"date-parts":[["2022",7,5]]},"language":"en","note":"Read_Status: New\nRead_Status_Date: 2025-12-19T09:17:14.594Z","number":"arXiv:2207.01813","publisher":"arXiv","source":"arXiv.org","title":"Stochastic Variational Methods in Generalized Hidden Semi-Markov Models to Characterize Functionality in Random Heteropolymers","type":"article","URL":"http://arxiv.org/abs/2207.01813"},{"id":"zitoMachineLearningApproach2023","abstract":"The ability to simulate gene expression and infer gene regulatory networks has vast potential applications in various fields, including medicine, agriculture, and environmental science. In recent years, machine learning approaches to simulate gene expression and infer gene regulatory networks have gained significant attention as a promising area of research. By simulating gene expression, we can gain insights into the complex mechanisms that control gene expression and how they are affected by various environmental factors. This knowledge can be used to develop new treatments for genetic diseases, improve crop yields, and better understand the evolution of species. In this article, we address this issue by focusing on a novel method capable of simulating the gene expression regulation of a group of genes and their mutual interactions. Our framework enables us to simulate the regulation of gene expression in response to alterations or perturbations that can affect the expression of a gene. We use both artificial and real benchmarks to empirically evaluate the effectiveness of our methodology. Furthermore, we compare our method with existing ones to understand its advantages and disadvantages. We also present future ideas for improvement to enhance the effectiveness of our method. Overall, our approach has the potential to greatly improve the field of gene expression simulation and gene regulatory network inference, possibly leading to significant advancements in genetics.","accessed":{"date-parts":[["2025",9,21]]},"author":[{"family":"Zito","given":"Francesco"},{"family":"Cutello","given":"Vincenzo"},{"family":"Pavone","given":"Mario"}],"citation-key":"zitoMachineLearningApproach2023","container-title":"Entropy","DOI":"10.3390/e25081214","ISSN":"1099-4300","issue":"8","issued":{"date-parts":[["2023",8]]},"language":"en","license":"http://creativecommons.org/licenses/by/3.0/","note":"Read_Status: New\nRead_Status_Date: 2025-09-23T11:03:33.438Z","page":"1214","publisher":"Multidisciplinary Digital Publishing Institute","source":"www.mdpi.com","title":"A Machine Learning Approach to Simulate Gene Expression and Infer Gene Regulatory Networks","type":"article-journal","URL":"https://www.mdpi.com/1099-4300/25/8/1214","volume":"25"},{"id":"zotero-item-771","citation-key":"zotero-item-771","note":"Read_Status: New\nRead_Status_Date: 2025-10-15T13:39:47.187Z","type":"book","URL":"https://www.jstor.org/stable/2029838"},{"id":"ZoteroConnectors","accessed":{"date-parts":[["2024",2,4]]},"citation-key":"ZoteroConnectors","title":"Zotero | Connectors","type":"webpage","URL":"https://www.zotero.org/download/connectors"},{"id":"ZoteroYourPersonal","accessed":{"date-parts":[["2025",5,5]]},"citation-key":"ZoteroYourPersonal","note":"Read_Status: New\nRead_Status_Date: 2025-05-05T07:36:49.504Z","title":"Zotero | Your personal research assistant","type":"webpage","URL":"https://www.zotero.org/download/"},{"id":"beaumontApproximateBayesianComputation2002","type":"article-journal","abstract":"We propose a new method for approximate Bayesian statistical inference on the basis of summary statistics. The method is suited to complex problems that arise in population genetics, extending ideas developed in this setting by earlier authors. Properties of the posterior distribution of a parameter, such as its mean or density curve, are approximated without explicit likelihood calculations. This is achieved by fitting a local-linear regression of simulated parameter values on simulated summary statistics, and then substituting the observed summary statistics into the regression equation. The method combines many of the advantages of Bayesian statistical inference with the computational efficiency of methods based on summary statistics. A key advantage of the method is that the nuisance parameters are automatically integrated out in the simulation step, so that the large numbers of nuisance parameters that arise in population genetics problems can be handled without difficulty. Simulation results indicate computational and statistical efficiency that compares favorably with those of alternative methods previously proposed in the literature. We also compare the relative efficiency of inferences obtained using methods based on summary statistics with those obtained directly from the data using MCMC.","citation-key":"beaumontApproximateBayesianComputation2002","container-title":"Genetics","DOI":"10.1093/genetics/162.4.2025","ISSN":"1943-2631","issue":"4","journalAbbreviation":"Genetics","page":"2025-2035","source":"Silverchair","title":"Approximate Bayesian Computation in Population Genetics","URL":"https://doi.org/10.1093/genetics/162.4.2025","volume":"162","author":[{"family":"Beaumont","given":"Mark A"},{"family":"Zhang","given":"Wenyang"},{"family":"Balding","given":"David J"}],"accessed":{"date-parts":[["2026",5,15]]},"issued":{"date-parts":[["2002",12,1]]},"library":"Ma bibliothèque","citekey":"beaumontApproximateBayesianComputation2002"}] |