@online{AccueilMIAParisSaclay,
  title = {Accueil | {{MIA Paris-Saclay}}},
  url = {https://mia-ps.inrae.fr/},
  urldate = {2023-07-03},
  file = {/home/polarolouis/Zotero/storage/I7FWTZC3/mia-ps.inrae.fr.html}
}

@online{anakokDisentanglingStructureEcological2022,
  title = {Disentangling the Structure of Ecological Bipartite Networks from Observation Processes},
  author = {Anakok, Emre and Barbillon, Pierre and Fontaine, Colin and Thebault, Elisa},
  date = {2022-11-29},
  eprint = {2211.16364},
  eprinttype = {arxiv},
  eprintclass = {stat},
  url = {http://arxiv.org/abs/2211.16364},
  urldate = {2023-06-14},
  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.},
  langid = {english},
  pubstate = {preprint},
  keywords = {Statistics - Methodology},
  file = {/home/polarolouis/Zotero/storage/LQ3FINZG/Anakok et al. - 2022 - Disentangling the structure of ecological bipartit.pdf}
}

@article{aubertModelbasedBiclusteringOverdispersed2021,
  title = {Model-Based Biclustering for Overdispersed Count Data with Application in Microbial Ecology},
  author = {Aubert, Julie and Schbath, Sophie and Robin, Stéphane},
  date = {2021},
  journaltitle = {Methods in Ecology and Evolution},
  volume = {12},
  number = {6},
  pages = {1050--1061},
  issn = {2041-210X},
  doi = {10.1111/2041-210X.13582},
  url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/2041-210X.13582},
  urldate = {2023-06-22},
  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).},
  langid = {english},
  keywords = {count data,latent block model,metabarcoding,microbial interactions,model-based biclustering,Poisson–Gamma distribution,variational EM algorithm},
  file = {/home/polarolouis/Zotero/storage/A4V9MJAF/Aubert et al. - 2021 - Model-based biclustering for overdispersed count d.pdf}
}

@inreference{AutoencodeurVariationnel2024,
  title = {Auto-encodeur variationnel},
  booktitle = {Wikipédia},
  date = {2024-03-13T19:44:29Z},
  url = {https://fr.wikipedia.org/w/index.php?title=Auto-encodeur_variationnel&oldid=213326719},
  urldate = {2024-05-21},
  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. Les VAE sont souvent rapprochés des autoencodeurs, en raison de leur architectures similaires. Leur utilisation et leur formulation mathématiques sont cependant différentes. Les 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.},
  langid = {french},
  annotation = {Page Version ID: 213326719},
  file = {/home/polarolouis/Zotero/storage/HLPG5K7Y/Auto-encodeur_variationnel.html}
}

@online{battagliaRelationalInductiveBiases2018,
  title = {Relational Inductive Biases, Deep Learning, and Graph Networks},
  author = {Battaglia, Peter W. and Hamrick, Jessica B. and Bapst, Victor and Sanchez-Gonzalez, Alvaro and Zambaldi, Vinicius and Malinowski, Mateusz and Tacchetti, Andrea and Raposo, David and Santoro, Adam and Faulkner, Ryan and Gulcehre, Caglar and Song, Francis and Ballard, Andrew and Gilmer, Justin and Dahl, George and Vaswani, Ashish and Allen, Kelsey and Nash, Charles and Langston, Victoria and Dyer, Chris and Heess, Nicolas and Wierstra, Daan and Kohli, Pushmeet and Botvinick, Matt and Vinyals, Oriol and Li, Yujia and Pascanu, Razvan},
  date = {2018-10-17},
  eprint = {1806.01261},
  eprinttype = {arxiv},
  eprintclass = {cs, stat},
  doi = {10.48550/arXiv.1806.01261},
  url = {http://arxiv.org/abs/1806.01261},
  urldate = {2024-05-15},
  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.},
  pubstate = {preprint},
  keywords = {Computer Science - Artificial Intelligence,Computer Science - Machine Learning,Statistics - Machine Learning},
  file = {/home/polarolouis/Zotero/storage/98Z2MFJP/Battaglia et al. - 2018 - Relational inductive biases, deep learning, and gr.pdf;/home/polarolouis/Zotero/storage/FIUI8TKL/1806.html}
}

@article{biernackiAssessingMixtureModel2000,
  title = {Assessing a Mixture Model for Clustering with the Integrated Completed Likelihood},
  author = {Biernacki, C. and Celeux, G. and Govaert, G.},
  date = {2000-07},
  journaltitle = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
  volume = {22},
  number = {7},
  pages = {719--725},
  issn = {1939-3539},
  doi = {10.1109/34.865189},
  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.},
  eventtitle = {{{IEEE Transactions}} on {{Pattern Analysis}} and {{Machine Intelligence}}},
  keywords = {Bayesian methods,Context modeling,Gaussian distribution,Numerical simulation,Probability distribution,Robustness},
  file = {/home/polarolouis/Zotero/storage/MK9H446U/Biernacki et al. - 2000 - Assessing a mixture model for clustering with the .pdf}
}

@article{botellaAppraisalGraphEmbeddings2022,
  title = {An Appraisal of Graph Embeddings for Comparing Trophic Network Architectures},
  author = {Botella, Christophe and Dray, Stéphane and Matias, Catherine and Miele, Vincent and Thuiller, Wilfried},
  date = {2022},
  journaltitle = {Methods in Ecology and Evolution},
  volume = {13},
  number = {1},
  pages = {203--216},
  issn = {2041-210X},
  doi = {10.1111/2041-210X.13738},
  url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/2041-210X.13738},
  urldate = {2024-05-14},
  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.},
  langid = {english},
  keywords = {dimension reduction,ecological interaction networks,evaluation,food webs,graph embedding,species interactions,trophic groups,trophic networks},
  file = {/home/polarolouis/Zotero/storage/P3KZ5UJ7/Botella et al. - 2022 - An appraisal of graph embeddings for comparing tro.pdf;/home/polarolouis/Zotero/storage/4HN89Q49/2041-210X.html}
}

@article{braultCoclusteringLatentBloc2015,
  title = {Co-clustering through Latent Bloc Model: a Review},
  shorttitle = {Co-clustering through Latent Bloc Model},
  author = {Brault, Vincent and Mariadassou, Mahendra},
  date = {2015},
  journaltitle = {Journal de la société française de statistique},
  volume = {156},
  number = {3},
  pages = {120--139},
  issn = {2102-6238},
  url = {http://www.numdam.org/item/JSFS_2015__156_3_120_0/},
  urldate = {2024-05-15},
  langid = {french},
  file = {/home/polarolouis/Zotero/storage/ZPMQXEIE/Brault et Mariadassou - 2015 - Co-clustering through Latent Bloc Model a Review.pdf}
}

@article{celisseConsistencyMaximumlikelihoodVariational2012,
  title = {Consistency of Maximum-Likelihood and Variational Estimators in the Stochastic Block Model},
  author = {Celisse, Alain and Daudin, Jean-Jacques and Pierre, Laurent},
  date = {2012-01},
  journaltitle = {Electronic Journal of Statistics},
  volume = {6},
  pages = {1847--1899},
  publisher = {{Institute of Mathematical Statistics and Bernoulli Society}},
  issn = {1935-7524, 1935-7524},
  doi = {10.1214/12-EJS729},
  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},
  urldate = {2023-06-06},
  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.},
  issue = {none},
  keywords = {62E17,62G05,62G20,62H30,Concentration inequalities,consistency,maximum likelihood estimators,Random graphs,Stochastic block model,variational estimators},
  file = {/home/polarolouis/Zotero/storage/JNWRIYKG/celisse2012.pdf.pdf;/home/polarolouis/Zotero/storage/XG463B5I/Celisse et al. - 2012 - Consistency of maximum-likelihood and variational .pdf}
}

@online{chabert-liddellLearningCommonStructures2023,
  type = {article},
  title = {Learning Common Structures in a Collection of Networks. {{An}} Application to Food Webs},
  author = {Chabert-Liddell, Saint-Clair and Barbillon, Pierre and Donnet, Sophie},
  date = {2023-03-27},
  eprint = {2206.00560},
  eprinttype = {arxiv},
  eprintclass = {stat},
  doi = {10.48550/arXiv.2206.00560},
  url = {http://arxiv.org/abs/2206.00560},
  urldate = {2023-05-22},
  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.},
  pubstate = {preprint},
  keywords = {Statistics - Applications,Statistics - Methodology},
  file = {/home/polarolouis/Zotero/storage/M74TXGCF/Chabert-Liddell et al. - 2023 - Learning common structures in a collection of netw.pdf;/home/polarolouis/Zotero/storage/A35M8KNP/2206.html}
}

@article{chabert-liddellLearningCommonStructures2024,
  title = {Learning Common Structures in a Collection of Networks. {{An}} Application to Food Webs},
  author = {Chabert-Liddell, Saint-Clair and Barbillon, Pierre and Donnet, Sophie},
  date = {2024-06},
  journaltitle = {The Annals of Applied Statistics},
  volume = {18},
  number = {2},
  pages = {1213--1235},
  publisher = {Institute of Mathematical Statistics},
  issn = {1932-6157, 1941-7330},
  doi = {10.1214/23-AOAS1831},
  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},
  urldate = {2024-05-16},
  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.},
  keywords = {clustering,ecology,latent variable models,networks,Stochastic block model},
  file = {/home/polarolouis/Zotero/storage/4USKD3WW/Chabert-Liddell et al. - 2024 - Learning common structures in a collection of netw.pdf}
}

@article{daigavaneUnderstandingConvolutionsGraphs2021,
  title = {Understanding {{Convolutions}} on {{Graphs}}},
  author = {Daigavane, Ameya and Ravindran, Balaraman and Aggarwal, Gaurav},
  date = {2021-09-02},
  journaltitle = {Distill},
  volume = {6},
  number = {9},
  pages = {e32},
  issn = {2476-0757},
  doi = {10.23915/distill.00032},
  url = {https://distill.pub/2021/understanding-gnns},
  urldate = {2024-05-21},
  abstract = {Understanding the building blocks and design choices of graph neural networks.},
  langid = {english},
  file = {/home/polarolouis/Zotero/storage/RBH4EXGY/understanding-gnns.html}
}

@article{daudinMixtureModelRandom2008,
  title = {A Mixture Model for Random Graphs},
  author = {Daudin, J.-J. and Picard, F. and Robin, S.},
  date = {2008-06-01},
  journaltitle = {Stat Comput},
  volume = {18},
  number = {2},
  pages = {173--183},
  issn = {1573-1375},
  doi = {10.1007/s11222-007-9046-7},
  url = {https://doi.org/10.1007/s11222-007-9046-7},
  urldate = {2023-06-16},
  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.},
  langid = {english},
  keywords = {Mixture models,Random graphs,Variational method},
  file = {/home/polarolouis/Zotero/storage/439HK27B/Daudin et al. - 2008 - A mixture model for random graphs.pdf;/home/polarolouis/Zotero/storage/HVVF5MNY/daudin2007.pdf.pdf}
}

@article{desjardins-proulxEcologicalInteractionsNetflix2017,
  title = {Ecological Interactions and the {{Netflix}} Problem},
  author = {Desjardins-Proulx, Philippe and Laigle, Idaline and Poisot, Timothée and Gravel, Dominique},
  date = {2017-08-10},
  journaltitle = {PeerJ},
  volume = {5},
  pages = {e3644},
  publisher = {PeerJ Inc.},
  issn = {2167-8359},
  doi = {10.7717/peerj.3644},
  url = {https://peerj.com/articles/3644},
  urldate = {2023-06-15},
  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.},
  langid = {english},
  file = {/home/polarolouis/Zotero/storage/3L7JALP4/Desjardins-Proulx et al. - 2017 - Ecological interactions and the Netflix problem.pdf}
}

@article{doreRelativeEffectsAnthropogenic2021,
  title = {Relative Effects of Anthropogenic Pressures, Climate, and Sampling Design on the Structure of Pollination Networks at the Global Scale},
  author = {Doré, Maël and Fontaine, Colin and Thébault, Elisa},
  date = {2021},
  journaltitle = {Global Change Biology},
  volume = {27},
  number = {6},
  pages = {1266--1280},
  issn = {1365-2486},
  doi = {10.1111/gcb.15474},
  url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/gcb.15474},
  urldate = {2023-06-21},
  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.},
  langid = {english},
  keywords = {anthropogenic pressures,climate,connectance,data,generalism,human impacts,plant-pollinator,pollination networks,richness,sampling effects,specialization},
  file = {/home/polarolouis/Zotero/storage/89ZXBJQP/10.1111@gcb.15474.pdf.pdf;/home/polarolouis/Zotero/storage/IVR6RGG7/Doré et al. - 2021 - Relative effects of anthropogenic pressures, clima.pdf;/home/polarolouis/Zotero/storage/WSJ4DV98/gcb.html}
}

@inproceedings{gilmerNeuralMessagePassing2017,
  title = {Neural {{Message Passing}} for {{Quantum Chemistry}}},
  booktitle = {Proceedings of the 34th {{International Conference}} on {{Machine Learning}}},
  author = {Gilmer, Justin and Schoenholz, Samuel S. and Riley, Patrick F. and Vinyals, Oriol and Dahl, George E.},
  date = {2017-07-17},
  pages = {1263--1272},
  publisher = {PMLR},
  issn = {2640-3498},
  url = {https://proceedings.mlr.press/v70/gilmer17a.html},
  urldate = {2024-05-15},
  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.},
  eventtitle = {International {{Conference}} on {{Machine Learning}}},
  langid = {english},
  file = {/home/polarolouis/Zotero/storage/B45XI65B/Gilmer et al. - 2017 - Neural Message Passing for Quantum Chemistry.pdf;/home/polarolouis/Zotero/storage/JNAIYUKE/Gilmer et al. - 2017 - Neural Message Passing for Quantum Chemistry.pdf}
}

@article{govaertEMAlgorithmBlock2005,
  title = {An {{EM}} Algorithm for the Block Mixture Model},
  author = {Govaert, G. and Nadif, M.},
  date = {2005-04},
  journaltitle = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
  volume = {27},
  number = {4},
  pages = {643--647},
  issn = {1939-3539},
  doi = {10.1109/TPAMI.2005.69},
  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.},
  eventtitle = {{{IEEE Transactions}} on {{Pattern Analysis}} and {{Machine Intelligence}}},
  keywords = {Approximation algorithms,Classification algorithms,Clustering algorithms,Clustering methods,Data mining,EM algorithm,Index Terms- Block mixture model,Maximum likelihood estimation,Parameter estimation,Partitioning algorithms,Self organizing feature maps,Sparse matrices,variational approximation.},
  file = {/home/polarolouis/Zotero/storage/6IG45HH2/govaert2005.pdf.pdf;/home/polarolouis/Zotero/storage/TL8M3XRF/Govaert et Nadif - 2005 - An EM algorithm for the block mixture model.pdf;/home/polarolouis/Zotero/storage/2Y48IB26/1401917.html}
}

@article{govaertLatentBlockModel2010,
  title = {Latent {{Block Model}} for {{Contingency Table}}},
  author = {Govaert, Gérard and Nadif, Mohamed},
  date = {2010-01-13},
  journaltitle = {Communications in Statistics - Theory and Methods},
  volume = {39},
  number = {3},
  pages = {416--425},
  publisher = {Taylor \& Francis},
  issn = {0361-0926},
  doi = {10.1080/03610920903140197},
  url = {https://doi.org/10.1080/03610920903140197},
  urldate = {2023-06-15},
  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.},
  keywords = {62H17,62H30,Block clustering,Block Poisson mixture model,CEM algorithm,Contingency table,EM algorithm},
  file = {/home/polarolouis/Zotero/storage/PPHP33Z9/Govaert et Nadif - 2010 - Latent Block Model for Contingency Table.pdf;/home/polarolouis/Zotero/storage/UT8TARCX/govaert2010.pdf.pdf}
}

@article{hamiltonInductiveRepresentationLearning,
  title = {Inductive {{Representation Learning}} on {{Large Graphs}}},
  author = {Hamilton, Will and Ying, Zhitao and Leskovec, Jure},
  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.},
  langid = {english},
  file = {/home/polarolouis/Zotero/storage/YIUG7VAU/Hamilton et al. - Inductive Representation Learning on Large Graphs.pdf}
}

@article{hoffLatentSpaceApproaches2002,
  title = {Latent {{Space Approaches}} to {{Social Network Analysis}}},
  author = {Hoff, Peter D and Raftery, Adrian E and Handcock, Mark S},
  date = {2002-12-01},
  journaltitle = {Journal of the American Statistical Association},
  volume = {97},
  number = {460},
  pages = {1090--1098},
  publisher = {Taylor \& Francis},
  issn = {0162-1459},
  doi = {10.1198/016214502388618906},
  url = {https://doi.org/10.1198/016214502388618906},
  urldate = {2024-05-20},
  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.},
  keywords = {Conditional independence model,Latent position model,Network data,Random graph,Visualization},
  file = {/home/polarolouis/Zotero/storage/7UYRBBA2/Hoff et al. - 2002 - Latent Space Approaches to Social Network Analysis.pdf;/home/polarolouis/Zotero/storage/R4TGSVGP/016214502388618906.pdf.pdf}
}

@article{hollandStochasticBlockmodelsFirst1983,
  title = {Stochastic Blockmodels: {{First}} Steps},
  shorttitle = {Stochastic Blockmodels},
  author = {Holland, Paul W. and Laskey, Kathryn Blackmond and Leinhardt, Samuel},
  date = {1983-06-01},
  journaltitle = {Social Networks},
  volume = {5},
  number = {2},
  pages = {109--137},
  issn = {0378-8733},
  doi = {10.1016/0378-8733(83)90021-7},
  url = {https://www.sciencedirect.com/science/article/pii/0378873383900217},
  urldate = {2023-06-15},
  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.},
  langid = {english},
  file = {/home/polarolouis/Zotero/storage/6F8YT8AD/holland1983.pdf.pdf;/home/polarolouis/Zotero/storage/7DSZ3KD9/Holland et al. - 1983 - Stochastic blockmodels First steps.pdf;/home/polarolouis/Zotero/storage/DUL2RV8Q/holland1983.pdf.pdf;/home/polarolouis/Zotero/storage/G9KZBG9W/0378873383900217.html}
}

@article{hubertComparingPartitions1985,
  title = {Comparing Partitions},
  author = {Hubert, Lawrence and Arabie, Phipps},
  date = {1985-12-01},
  journaltitle = {Journal of Classification},
  volume = {2},
  number = {1},
  pages = {193--218},
  issn = {1432-1343},
  doi = {10.1007/BF01908075},
  url = {https://doi.org/10.1007/BF01908075},
  urldate = {2023-07-04},
  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.},
  langid = {english},
  keywords = {Consensus indices,Measures of agreement,Measures of association},
  file = {/home/polarolouis/Zotero/storage/7TKW7HEM/Hubert et Arabie - 1985 - Comparing partitions.pdf}
}

@article{kaszewska-gilasGlobalStudiesHostParasite2021,
  title = {Global {{Studies}} of the {{Host-Parasite Relationships}} between {{Ectoparasitic Mites}} of the {{Family Syringophilidae}} and {{Birds}} of the {{Order Columbiformes}}},
  author = {Kaszewska-Gilas, Katarzyna and Kosicki, Jakub Ziemowit and Hromada, Martin and Skoracki, Maciej},
  date = {2021-12},
  journaltitle = {Animals},
  volume = {11},
  number = {12},
  pages = {3392},
  publisher = {Multidisciplinary Digital Publishing Institute},
  issn = {2076-2615},
  doi = {10.3390/ani11123392},
  url = {https://www.mdpi.com/2076-2615/11/12/3392},
  urldate = {2023-06-15},
  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.},
  issue = {12},
  langid = {english},
  keywords = {Acari,biodiversity,bipartite-example,network,pigeons and doves,quill mites},
  file = {/home/polarolouis/Zotero/storage/VXVQ5CPH/Kaszewska-Gilas et al. - 2021 - Global Studies of the Host-Parasite Relationships .pdf}
}

@article{keribinEstimationSelectionLatent2015,
  title = {Estimation and Selection for the Latent Block Model on Categorical Data},
  author = {Keribin, Christine and Brault, Vincent and Celeux, Gilles and Govaert, Gérard},
  date = {2015-11-01},
  journaltitle = {Stat Comput},
  volume = {25},
  number = {6},
  pages = {1201--1216},
  issn = {1573-1375},
  doi = {10.1007/s11222-014-9472-2},
  url = {https://doi.org/10.1007/s11222-014-9472-2},
  urldate = {2024-05-15},
  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.},
  langid = {english},
  keywords = {Bayesian inference,BIC criterion,EM algorithm,Gibbs sampling,Integrated completed likelihood,Stochastic EM,Variational approximation},
  file = {/home/polarolouis/Zotero/storage/49IKUHMA/s11222-014-9472-2.pdf.pdf;/home/polarolouis/Zotero/storage/VXKAK359/Keribin et al. - 2015 - Estimation and selection for the latent block mode.pdf}
}

@online{kingmaAutoEncodingVariationalBayes2022c,
  title = {Auto-{{Encoding Variational Bayes}}},
  author = {Kingma, Diederik P. and Welling, Max},
  date = {2022-12-10},
  eprint = {1312.6114},
  eprinttype = {arxiv},
  eprintclass = {cs, stat},
  doi = {10.48550/arXiv.1312.6114},
  url = {http://arxiv.org/abs/1312.6114},
  urldate = {2024-05-21},
  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.},
  pubstate = {preprint},
  keywords = {Computer Science - Machine Learning,Statistics - Machine Learning},
  file = {/home/polarolouis/Zotero/storage/ECSHPY3J/Kingma et Welling - 2022 - Auto-Encoding Variational Bayes.pdf;/home/polarolouis/Zotero/storage/MK5TFH4E/1312.html}
}

@online{kipfSemiSupervisedClassificationGraph2017,
  title = {Semi-{{Supervised Classification}} with {{Graph Convolutional Networks}}},
  author = {Kipf, Thomas N. and Welling, Max},
  date = {2017-02-22},
  eprint = {1609.02907},
  eprinttype = {arxiv},
  eprintclass = {cs, stat},
  doi = {10.48550/arXiv.1609.02907},
  url = {http://arxiv.org/abs/1609.02907},
  urldate = {2024-05-14},
  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.},
  pubstate = {preprint},
  keywords = {Computer Science - Machine Learning,Statistics - Machine Learning},
  file = {/home/polarolouis/Zotero/storage/SWWT37XC/Kipf et Welling - 2017 - Semi-Supervised Classification with Graph Convolut.pdf;/home/polarolouis/Zotero/storage/6XSQ5U3D/1609.html}
}

@online{kipfVariationalGraphAutoEncoders2016,
  title = {Variational {{Graph Auto-Encoders}}},
  author = {Kipf, Thomas N. and Welling, Max},
  date = {2016-11-21},
  eprint = {1611.07308},
  eprinttype = {arxiv},
  eprintclass = {cs, stat},
  doi = {10.48550/arXiv.1611.07308},
  url = {http://arxiv.org/abs/1611.07308},
  urldate = {2024-05-14},
  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.},
  pubstate = {preprint},
  keywords = {Computer Science - Machine Learning,Statistics - Machine Learning},
  file = {/home/polarolouis/Zotero/storage/MSK48ZUE/Kipf et Welling - 2016 - Variational Graph Auto-Encoders.pdf;/home/polarolouis/Zotero/storage/3VJBSGI3/1611.html}
}

@online{larousseDefinitionsBipartiBipartite,
  title = {Définitions : biparti, bipartite - Dictionnaire de français Larousse},
  shorttitle = {Définitions},
  author = {Larousse, Éditions},
  url = {https://www.larousse.fr/dictionnaires/francais/biparti/9503},
  urldate = {2023-06-17},
  abstract = {biparti, bipartite - Définitions Français : Retrouvez la définition de biparti, bipartite, ainsi que les difficultés... - synonymes, homonymes, difficultés, citations.},
  langid = {french},
  file = {/home/polarolouis/Zotero/storage/MA2VH6NX/9503.html}
}

@article{maeldoreMaelDorePollination_networksScripts2020,
  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},
  shorttitle = {{{MaelDore}}/{{Pollination}}\_networks},
  author = {MaelDore},
  date = {2020-11-25},
  publisher = {Zenodo},
  doi = {10.5281/ZENODO.4290503},
  url = {https://zenodo.org/record/4290503},
  urldate = {2023-06-21},
  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},
  keywords = {data,plant-pollinator}
}

@article{matchadoNetworkAnalysisMethods2021,
  title = {Network Analysis Methods for Studying Microbial Communities: {{A}} Mini Review},
  shorttitle = {Network Analysis Methods for Studying Microbial Communities},
  author = {Matchado, Monica Steffi and Lauber, Michael and Reitmeier, Sandra and Kacprowski, Tim and Baumbach, Jan and Haller, Dirk and List, Markus},
  date = {2021-01-01},
  journaltitle = {Computational and Structural Biotechnology Journal},
  volume = {19},
  pages = {2687--2698},
  issn = {2001-0370},
  doi = {10.1016/j.csbj.2021.05.001},
  url = {https://www.sciencedirect.com/science/article/pii/S2001037021001823},
  urldate = {2024-05-16},
  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.},
  keywords = {Microbial co-occurrence networks,Microbial interactions,Network analysis,Trans-kingdom interactions},
  file = {/home/polarolouis/Zotero/storage/NAEQFHE8/j.csbj.2021.05.001.pdf.pdf;/home/polarolouis/Zotero/storage/SXJYNPP7/Matchado et al. - 2021 - Network analysis methods for studying microbial co.pdf;/home/polarolouis/Zotero/storage/B6NZVP7Y/S2001037021001823.html}
}

@book{ottawafield-naturalistsclubCanadianFieldnaturalist1976,
  title = {The {{Canadian}} Field-Naturalist},
  author = {Ottawa Field-Naturalists' Club and Club, Ottawa Field-Naturalists'},
  date = {1976},
  volume = {90},
  pages = {1--568},
  publisher = {Ottawa Field-Naturalists' Club},
  location = {Ottawa},
  issn = {0008-3550},
  url = {https://www.biodiversitylibrary.org/item/89149},
  pagetotal = {568},
  file = {/home/polarolouis/Zotero/storage/DFN9BYBR/28045499.html}
}

@article{pavlopoulosBipartiteGraphsSystems2018,
  title = {Bipartite Graphs in Systems Biology and Medicine: A Survey of Methods and Applications},
  shorttitle = {Bipartite Graphs in Systems Biology and Medicine},
  author = {Pavlopoulos, Georgios A and Kontou, Panagiota I and Pavlopoulou, Athanasia and Bouyioukos, Costas and Markou, Evripides and Bagos, Pantelis G},
  date = {2018-04-01},
  journaltitle = {GigaScience},
  volume = {7},
  number = {4},
  pages = {giy014},
  issn = {2047-217X},
  doi = {10.1093/gigascience/giy014},
  url = {https://doi.org/10.1093/gigascience/giy014},
  urldate = {2023-06-15},
  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.},
  file = {/home/polarolouis/Zotero/storage/2KJFL3SB/Pavlopoulos et al. - 2018 - Bipartite graphs in systems biology and medicine .pdf;/home/polarolouis/Zotero/storage/A2Y2EGPA/pavlopoulos2018.pdf.pdf;/home/polarolouis/Zotero/storage/UK2MK5FW/pavlopoulos2018.pdf.pdf;/home/polarolouis/Zotero/storage/XP7G4PZF/4875933.html}
}

@online{peyreComputationalOptimalTransport2020,
  title = {Computational {{Optimal Transport}}},
  author = {Peyré, Gabriel and Cuturi, Marco},
  date = {2020-03-18},
  eprint = {1803.00567},
  eprinttype = {arxiv},
  eprintclass = {stat},
  doi = {10.48550/arXiv.1803.00567},
  url = {http://arxiv.org/abs/1803.00567},
  urldate = {2024-05-14},
  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.},
  pubstate = {preprint},
  keywords = {Statistics - Machine Learning},
  file = {/home/polarolouis/Zotero/storage/64Q9WE2Z/Peyré et Cuturi - 2020 - Computational Optimal Transport.pdf;/home/polarolouis/Zotero/storage/3GAQMNL8/1803.html}
}

@article{ramos-jilibertoTopologicalChangeAndean2010,
  title = {Topological Change of {{Andean}} Plant–Pollinator Networks along an Altitudinal Gradient},
  author = {Ramos-Jiliberto, Rodrigo and Domínguez, Daniela and Espinoza, Claudia and López, Gioconda and Valdovinos, Fernanda S. and Bustamante, Ramiro O. and Medel, Rodrigo},
  date = {2010-03-01},
  journaltitle = {Ecological Complexity},
  volume = {7},
  number = {1},
  pages = {86--90},
  issn = {1476-945X},
  doi = {10.1016/j.ecocom.2009.06.001},
  url = {https://www.sciencedirect.com/science/article/pii/S1476945X09000622},
  urldate = {2023-06-15},
  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.},
  langid = {english},
  keywords = {bipartite-example,Chile,Complexity,Degree distribution,Modularity,Mutualistic networks,Nestedness,Power law},
  file = {/home/polarolouis/Zotero/storage/ATY3ZP2X/Ramos-Jiliberto et al. - 2010 - Topological change of Andean plant–pollinator netw.pdf;/home/polarolouis/Zotero/storage/HPBGUP65/ramos-jiliberto2010.pdf.pdf;/home/polarolouis/Zotero/storage/I33MZQQ7/ramos-jiliberto2010.pdf.pdf;/home/polarolouis/Zotero/storage/YJX8XBNW/S1476945X09000622.html}
}

@article{sanchez-lengelingGentleIntroductionGraph2021,
  title = {A {{Gentle Introduction}} to {{Graph Neural Networks}}},
  author = {Sanchez-Lengeling, Benjamin and Reif, Emily and Pearce, Adam and Wiltschko, Alexander B.},
  date = {2021-09-02},
  journaltitle = {Distill},
  volume = {6},
  number = {9},
  pages = {e33},
  issn = {2476-0757},
  doi = {10.23915/distill.00033},
  url = {https://distill.pub/2021/gnn-intro},
  urldate = {2024-05-15},
  abstract = {What components are needed for building learning algorithms that leverage the structure and properties of graphs?},
  langid = {english},
  file = {/home/polarolouis/Zotero/storage/4A3V4EFV/gnn-intro.html}
}

@article{sewellLatentSpaceModels2015,
  title = {Latent {{Space Models}} for {{Dynamic Networks}}},
  author = {Sewell, Daniel K. and Chen, Yuguo},
  date = {2015-10-02},
  journaltitle = {Journal of the American Statistical Association},
  volume = {110},
  number = {512},
  pages = {1646--1657},
  publisher = {Taylor \& Francis},
  issn = {0162-1459},
  doi = {10.1080/01621459.2014.988214},
  url = {https://www.tandfonline.com/doi/full/10.1080/01621459.2014.988214},
  urldate = {2024-05-20},
  keywords = {Embedding,Markov chain Monte Carlo,Network data,Social influence,Visualization},
  file = {/home/polarolouis/Zotero/storage/6LJMITGR/Sewell et Chen - 2015 - Latent Space Models for Dynamic Networks.pdf;/home/polarolouis/Zotero/storage/TJD8ZWBA/01621459.2014.988214.pdf.pdf}
}

@article{snijdersEstimationPredictionStochastic1997,
  title = {Estimation and {{Prediction}} for {{Stochastic Blockmodels}} for {{Graphs}} with {{Latent Block Structure}}},
  author = {Snijders, Tom A.B. and Nowicki, Krzysztof},
  date = {1997-01-01},
  journaltitle = {J. of Classification},
  volume = {14},
  number = {1},
  pages = {75--100},
  issn = {1432-1343},
  doi = {10.1007/s003579900004},
  url = {https://doi.org/10.1007/s003579900004},
  urldate = {2023-06-15},
  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.},
  langid = {english},
  keywords = {Bayesian Estimator,Block Structure,Gibbs Sampling,Large Graph,Statistical Procedure},
  file = {/home/polarolouis/Zotero/storage/2GYRASW5/snijders1997.pdf.pdf;/home/polarolouis/Zotero/storage/JJNQV32Y/Snijders et Nowicki - 1997 - Estimation and Prediction for Stochastic Blockmode.pdf;/home/polarolouis/Zotero/storage/LXGG9SRP/snijders1997.pdf.pdf}
}

@misc{Sujetthese,
  title = {Sujet-These},
  file = {/home/polarolouis/Nextcloud/Documents/APT/Thèse/Administratif/candidature-these/Sujet.pdf}
}

@dataset{thebaultDatabasePlantpollinatorNetworks2020,
  title = {A Database of Plant-Pollinator Networks},
  author = {Thébault, Elisa and Fontaine, Colin},
  date = {2020-12-01},
  publisher = {Zenodo},
  doi = {10.5281/zenodo.4300427},
  url = {https://zenodo.org/record/4300427},
  urldate = {2023-06-21},
  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.},
  version = {1},
  keywords = {diversity,flower visitors,mutualistic network,plant-pollinator interaction}
}

@dataset{thebaultelisaDatabasePlantpollinatorNetworks2020,
  title = {A Database of Plant-Pollinator Networks},
  author = {Thébault, Elisa and Fontaine, Colin},
  date = {2020-12-01},
  publisher = {Zenodo},
  doi = {10.5281/ZENODO.4300427},
  url = {https://zenodo.org/record/4300427},
  urldate = {2023-06-21},
  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.},
  version = {1},
  keywords = {data,diversity,flower visitors,mutualistic network,plant-pollinator,plant-pollinator interaction}
}

@dataset{thebaultelisaDatabasePlantpollinatorNetworks2022,
  title = {A Database of Plant-Pollinator Networks},
  author = {Thébault, Elisa and Fontaine, Colin},
  namea = {Doré, Maël and Parra, Santiago},
  nameatype = {collaborator},
  date = {2022-06-10},
  publisher = {Zenodo},
  doi = {10.5281/ZENODO.4300426},
  url = {https://zenodo.org/record/4300426},
  urldate = {2023-06-21},
  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.},
  version = {2},
  keywords = {data,diversity,flower visitors,mutualistic network,plant-pollinator,plant-pollinator interaction}
}

@dataset{thebaultelisaDatabasePlantpollinatorNetworks2022a,
  title = {A Database of Plant-Pollinator Networks},
  author = {Thébault, Elisa and Fontaine, Colin},
  namea = {Doré, Maël and Parra, Santiago},
  nameatype = {collaborator},
  date = {2022-06-10},
  publisher = {Zenodo},
  doi = {10.5281/ZENODO.6630184},
  url = {https://zenodo.org/record/6630184},
  urldate = {2023-06-21},
  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.},
  version = {2},
  keywords = {data,diversity,flower visitors,mutualistic network,plant-pollinator,plant-pollinator interaction}
}

@online{velickovicGraphAttentionNetworks2018,
  title = {Graph {{Attention Networks}}},
  author = {Veličković, Petar and Cucurull, Guillem and Casanova, Arantxa and Romero, Adriana and Liò, Pietro and Bengio, Yoshua},
  date = {2018-02-04},
  eprint = {1710.10903},
  eprinttype = {arxiv},
  eprintclass = {cs, stat},
  url = {http://arxiv.org/abs/1710.10903},
  urldate = {2024-05-14},
  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).},
  langid = {english},
  pubstate = {preprint},
  keywords = {Computer Science - Artificial Intelligence,Computer Science - Machine Learning,Computer Science - Social and Information Networks,Statistics - Machine Learning},
  file = {/home/polarolouis/Zotero/storage/8LZB7KTU/Veličković et al. - 2018 - Graph Attention Networks.pdf}
}

@online{WebLifeEcological,
  title = {Web of {{Life}}: Ecological Networks Database},
  url = {https://www.web-of-life.es/map.php},
  urldate = {2023-06-17},
  keywords = {networks,site},
  file = {/home/polarolouis/Zotero/storage/9WZE8QLQ/map.html}
}

@online{xuHowPowerfulAre2019,
  title = {How {{Powerful}} Are {{Graph Neural Networks}}?},
  author = {Xu, Keyulu and Hu, Weihua and Leskovec, Jure and Jegelka, Stefanie},
  date = {2019-02-22},
  eprint = {1810.00826},
  eprinttype = {arxiv},
  eprintclass = {cs, stat},
  doi = {10.48550/arXiv.1810.00826},
  url = {http://arxiv.org/abs/1810.00826},
  urldate = {2024-05-14},
  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.},
  pubstate = {preprint},
  keywords = {Computer Science - Computer Vision and Pattern Recognition,Computer Science - Machine Learning,Statistics - Machine Learning},
  file = {/home/polarolouis/Zotero/storage/THBD5QV3/Xu et al. - 2019 - How Powerful are Graph Neural Networks.pdf;/home/polarolouis/Zotero/storage/ZJF5UWIH/1810.html}
}

@article{yangDeepLatentSpace2024,
  title = {A {{Deep Latent Space Model}} for {{Graph Representation Learning}}},
  author = {Yang, Hanxuan and Kong, Qingchao and Mao, Wenji},
  date = {2024-04},
  journaltitle = {Neurocomputing},
  volume = {576},
  eprint = {2106.11721},
  eprinttype = {arxiv},
  eprintclass = {cs, stat},
  pages = {127342},
  issn = {09252312},
  doi = {10.1016/j.neucom.2024.127342},
  url = {http://arxiv.org/abs/2106.11721},
  urldate = {2024-05-20},
  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.},
  langid = {english},
  keywords = {Computer Science - Machine Learning,Statistics - Machine Learning},
  file = {/home/polarolouis/Zotero/storage/XNVMI2D7/Yang et al. - 2024 - A Deep Latent Space Model for Graph Representation.pdf}
}

@online{yumpu.comInsectPollinatorsMer,
  title = {Insect Pollinators of the {{Mer Bleue}} Peat Bog of {{Ottawa}} - {{Biodiversity}} ...},
  author = {Yumpu.com},
  url = {https://www.yumpu.com/en/document/view/11762821/insect-pollinators-of-the-mer-bleue-peat-bog-of-ottawa-biodiversity-},
  urldate = {2023-08-06},
  abstract = {Insect pollinators of the Mer Bleue peat bog of Ottawa - Biodiversity ...},
  langid = {english},
  organization = {yumpu.com},
  file = {/home/polarolouis/Zotero/storage/DIXT2PYL/insect-pollinators-of-the-mer-bleue-peat-bog-of-ottawa-biodiversity-.html}
}
