presentation-colbisbm/references.bib

@online{AccueilMIAParisSaclay,
  title = {Accueil | {{MIA Paris-Saclay}}},
  url = {https://mia-ps.inrae.fr/},
  urldate = {2023-07-03},
  file = {/home/louis/snap/zotero-snap/common/Zotero/storage/I7FWTZC3/mia-ps.inrae.fr.html}
}
@article{puTreeEnhancedLatentSpace2025,
  title        = {Tree-{{Enhanced Latent Space Models}} for {{Two-Mode Networks}}},
  author       = {Pu, Dan and Fan, Xinyan and Fang, Kuangnan},
  date         = {2025-06-21},
  journaltitle = {Journal of Computational and Graphical Statistics},
  volume       = {0},
  number       = {0},
  pages        = {1--9},
  publisher    = {ASA Website},
  issn         = {1061-8600},
  doi          = {10.1080/10618600.2025.2527295},
  url          = {https://doi.org/10.1080/10618600.2025.2527295},
  urldate      = {2025-09-23},
  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.},
  keywords     = {Latent space model,Tree-structured information,Two-mode network},
  annotation   = {Read\_Status: Read\\
                  Read\_Status\_Date: 2025-09-24T13:31:51.261Z},
  file         = {/home/louis/snap/zotero-snap/common/Zotero/storage/9DVZDMA7/Appendix.pdf;/home/louis/snap/zotero-snap/common/Zotero/storage/NK5GZXTL/Pu et al. - Tree-Enhanced Latent Space Models for Two-Mode Networks.pdf}
}

@article{wilmsTreebasedNodeAggregation2022,
  title        = {Tree-Based {{Node Aggregation}} in {{Sparse Graphical Models}}},
  author       = {Wilms, Ines and Bien, Jacob},
  date         = {2022},
  journaltitle = {Journal of Machine Learning Research},
  volume       = {23},
  number       = {243},
  pages        = {1--36},
  issn         = {1533-7928},
  url          = {http://jmlr.org/papers/v23/21-0105.html},
  urldate      = {2025-10-14},
  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.},
  annotation   = {Read\_Status: New\\
                  Read\_Status\_Date: 2025-10-14T12:07:34.769Z},
  file         = {/home/louis/snap/zotero-snap/common/Zotero/storage/V5KN62CY/Wilms et Bien - 2022 - Tree-based Node Aggregation in Sparse Graphical Models.pdf;/home/louis/snap/zotero-snap/common/Zotero/storage/4A8U5CT3/21-0105.html}
}
@incollection{AgglomerativeNestingProgram1990,
  title = {Agglomerative {{Nesting}} ({{Program AGNES}})},
  booktitle = {Finding {{Groups}} in {{Data}}},
  date = {1990},
  pages = {199--252},
  publisher = {John Wiley \& Sons, Ltd},
  doi = {10.1002/9780470316801.ch5},
  url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/9780470316801.ch5},
  urldate = {2024-09-13},
  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},
  isbn = {978-0-470-31680-1},
  langid = {english},
  keywords = {agglomerative nesting,data set,dissimilarity matrix,graphical representations,interval-scaled variables},
  file = {/home/louis/snap/zotero-snap/common/Zotero/storage/CVXNL7SP/1990 - Agglomerative Nesting (Program AGNES).pdf}
}

@article{agterbergJointSpectralClustering2025,
  title = {Joint {{Spectral Clustering}} in {{Multilayer Degree-Corrected Stochastic Blockmodels}}},
  author = {Agterberg, Joshua and Lubberts, Zachary and Arroyo, Jesús},
  date = {2025-04},
  journaltitle = {Journal of the American Statistical Association},
  volume = {0},
  number = {0},
  pages = {1--15},
  publisher = {ASA Website},
  issn = {0162-1459},
  doi = {10.1080/01621459.2025.2516201},
  url = {https://doi.org/10.1080/01621459.2025.2516201},
  urldate = {2025-09-19},
  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.},
  keywords = {/unread},
  annotation = {Read\_Status: New\\
Read\_Status\_Date: 2025-09-19T13:53:26.541Z},
  file = {/home/louis/snap/zotero-snap/common/Zotero/storage/C82RAE8U/Agterberg et al. - Joint Spectral Clustering in Multilayer Degree-Corrected Stochastic Blockmodels.pdf}
}

@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 = {prepublished},
  keywords = {Statistics - Methodology},
  file = {/home/louis/snap/zotero-snap/common/Zotero/storage/LQ3FINZG/Anakok et al. - 2022 - Disentangling the structure of ecological bipartit.pdf}
}

@incollection{Appendix1990,
  title = {Appendix},
  booktitle = {Finding {{Groups}} in {{Data}}},
  date = {1990},
  pages = {312--319},
  publisher = {John Wiley \& Sons, Ltd},
  doi = {10.1002/9780470316801.app1},
  url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/9780470316801.app1},
  urldate = {2024-09-13},
  isbn = {978-0-470-31680-1},
  langid = {english},
  file = {/home/louis/snap/zotero-snap/common/Zotero/storage/RPETTM3Z/1990 - Appendix.pdf}
}

@article{arroyoInferenceMultipleHeterogeneous2021,
  title = {Inference for {{Multiple Heterogeneous Networks}} with a {{Common Invariant Subspace}}},
  author = {Arroyo, Jesús and Athreya, Avanti and Cape, Joshua and Chen, Guodong and Priebe, Carey E. and Vogelstein, Joshua T.},
  date = {2021},
  journaltitle = {Journal of Machine Learning Research},
  volume = {22},
  number = {142},
  pages = {1--49},
  issn = {1533-7928},
  url = {http://jmlr.org/papers/v22/19-558.html},
  urldate = {2025-09-19},
  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.},
  keywords = {/unread},
  annotation = {Read\_Status: New\\
Read\_Status\_Date: 2025-09-19T14:02:30.452Z},
  file = {/home/louis/snap/zotero-snap/common/Zotero/storage/DAGYGPA3/Arroyo et al. - 2021 - Inference for Multiple Heterogeneous Networks with a Common Invariant Subspace.pdf;/home/louis/snap/zotero-snap/common/Zotero/storage/RPJ3SJGW/19-558.html}
}

@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/louis/snap/zotero-snap/common/Zotero/storage/A4V9MJAF/Aubert et al. - 2021 - Model-based biclustering for overdispersed count d.pdf}
}

@incollection{AuthorIndex1990,
  title = {Author {{Index}}},
  booktitle = {Finding {{Groups}} in {{Data}}},
  date = {1990},
  pages = {322--335},
  publisher = {John Wiley \& Sons, Ltd},
  doi = {10.1002/9780470316801.indauth},
  url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/9780470316801.indauth},
  urldate = {2024-09-13},
  isbn = {978-0-470-31680-1},
  langid = {english},
  file = {/home/louis/snap/zotero-snap/common/Zotero/storage/S9F7YWH4/1990 - Author Index.pdf}
}

@article{baldockDailyTemporalStructure2011,
  title = {Daily Temporal Structure in {{African}} Savanna Flower Visitation Networks and Consequences for Network Sampling},
  author = {Baldock, Katherine C. R. and Memmott, Jane and Ruiz-Guajardo, Juan Carlos and Roze, Denis and Stone, Graham N.},
  date = {2011},
  journaltitle = {Ecology},
  volume = {92},
  number = {3},
  pages = {687--698},
  issn = {1939-9170},
  doi = {10.1890/10-1110.1},
  url = {https://onlinelibrary.wiley.com/doi/abs/10.1890/10-1110.1},
  urldate = {2024-07-02},
  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.},
  langid = {english},
  keywords = {Africa,competition,ecological networks,facilitation,Kenya,mutualism,pollination,savanna,temporal structure,visitation webs},
  file = {/home/louis/snap/zotero-snap/common/Zotero/storage/4ALS9Y6W/10-1110.1.pdf.pdf;/home/louis/snap/zotero-snap/common/Zotero/storage/4YSLVYC5/Baldock et al. - 2011 - Daily temporal structure in African savanna flower.pdf;/home/louis/snap/zotero-snap/common/Zotero/storage/7PEDTWU9/10-1110.html}
}

@article{baldockSystemsApproachReveals2019,
  title = {A Systems Approach Reveals Urban Pollinator Hotspots and Conservation Opportunities},
  author = {Baldock, Katherine C. R. and Goddard, Mark A. and Hicks, Damien M. and Kunin, William E. and Mitschunas, Nadine and Morse, Helen and Osgathorpe, Lynne M. and Potts, Simon G. and Robertson, Kirsty M. and Scott, Anna V. and Staniczenko, Phillip P. A. and Stone, Graham N. and Vaughan, Ian P. and Memmott, Jane},
  date = {2019-03},
  journaltitle = {Nature Ecology \& Evolution},
  shortjournal = {Nat Ecol Evol},
  volume = {3},
  number = {3},
  eprint = {30643247},
  eprinttype = {pubmed},
  pages = {363--373},
  issn = {2397-334X},
  doi = {10.1038/s41559-018-0769-y},
  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.},
  langid = {english},
  pmcid = {PMC6445365},
  keywords = {Bayes Theorem,Biodiversity,Cities,Conservation of Natural Resources,England,Pollination,Scotland,Systems Analysis},
  file = {/home/louis/snap/zotero-snap/common/Zotero/storage/BSGKKFLX/s41559-018-0769-y.pdf.pdf;/home/louis/snap/zotero-snap/common/Zotero/storage/NZR8WPUA/Baldock et al. - 2019 - A systems approach reveals urban pollinator hotspo.pdf}
}

@article{beauguitteLanalyseGraphesBipartis,
  title = {L'analyse des graphes bipartis},
  author = {Beauguitte, Laurent},
  langid = {french},
  file = {/home/louis/snap/zotero-snap/common/Zotero/storage/JN3DD4XS/Beauguitte - L'analyse des graphes bipartis.pdf}
}

@article{bickelNonparametricViewNetwork2009,
  title = {A Nonparametric View of Network Models and {{Newman}}–{{Girvan}} and Other Modularities},
  author = {Bickel, Peter J. and Chen, Aiyou},
  date = {2009-12-15},
  journaltitle = {Proceedings of the National Academy of Sciences},
  shortjournal = {Proc. Natl. Acad. Sci. U.S.A.},
  volume = {106},
  number = {50},
  pages = {21068--21073},
  issn = {0027-8424, 1091-6490},
  doi = {10.1073/pnas.0907096106},
  url = {https://pnas.org/doi/full/10.1073/pnas.0907096106},
  urldate = {2024-11-22},
  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.},
  langid = {english},
  file = {/home/louis/snap/zotero-snap/common/Zotero/storage/VFL87V9L/Bickel et Chen - 2009 - A nonparametric view of network models and Newman–Girvan and other modularities.pdf}
}

@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/louis/snap/zotero-snap/common/Zotero/storage/MK9H446U/Biernacki et al. - 2000 - Assessing a mixture model for clustering with the .pdf}
}

@article{boschPlantPollinatorNetworks2009,
  title = {Plant–Pollinator Networks: Adding the Pollinator’s Perspective},
  shorttitle = {Plant–Pollinator Networks},
  author = {Bosch, Jordi and Martín González, Ana M. and Rodrigo, Anselm and Navarro, David},
  date = {2009},
  journaltitle = {Ecology Letters},
  volume = {12},
  number = {5},
  pages = {409--419},
  issn = {1461-0248},
  doi = {10.1111/j.1461-0248.2009.01296.x},
  url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1461-0248.2009.01296.x},
  urldate = {2024-08-20},
  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.},
  langid = {english},
  keywords = {Apparent specialization,coevolution,generalization,modularity,nestedness,plant–pollinator interactions,pollen analysis,pollination web,sampling effort},
  file = {/home/louis/snap/zotero-snap/common/Zotero/storage/C5TQ6Y49/Bosch et al. - 2009 - Plant–pollinator networks adding the pollinator’s perspective.pdf;/home/louis/snap/zotero-snap/common/Zotero/storage/BHMVU3DU/j.1461-0248.2009.01296.html}
}

@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/louis/snap/zotero-snap/common/Zotero/storage/JNWRIYKG/celisse2012.pdf.pdf;/home/louis/snap/zotero-snap/common/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 = {prepublished},
  keywords = {Statistics - Applications,Statistics - Methodology},
  file = {/home/louis/snap/zotero-snap/common/Zotero/storage/M74TXGCF/Chabert-Liddell et al. - 2023 - Learning common structures in a collection of netw.pdf;/home/louis/snap/zotero-snap/common/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 = {2024-07-01},
  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/louis/snap/zotero-snap/common/Zotero/storage/9XBNTTWB/Chabert-Liddell et al. - 2024 - Learning common structures in a collection of netw.pdf}
}

@article{chabert-liddellStochasticBlockModel2021,
  title = {A {{Stochastic Block Model Approach}} for the {{Analysis}} of {{Multilevel Networks}}: An {{Application}} to the {{Sociology}} of {{Organizations}}},
  shorttitle = {A {{Stochastic Block Model Approach}} for the {{Analysis}} of {{Multilevel Networks}}},
  author = {Chabert-Liddell, Saint-Clair and Barbillon, Pierre and Donnet, Sophie and Lazega, Emmanuel},
  date = {2021-06},
  journaltitle = {Computational Statistics \& Data Analysis},
  shortjournal = {Computational Statistics \& Data Analysis},
  volume = {158},
  eprint = {1910.10512},
  eprinttype = {arXiv},
  eprintclass = {stat},
  pages = {107179},
  issn = {01679473},
  doi = {10.1016/j.csda.2021.107179},
  url = {http://arxiv.org/abs/1910.10512},
  urldate = {2025-09-26},
  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.},
  keywords = {/unread,Computer Science - Social and Information Networks,Statistics - Applications,Statistics - Methodology},
  annotation = {Read\_Status: New\\
Read\_Status\_Date: 2025-09-26T08:52:07.522Z},
  file = {/home/louis/snap/zotero-snap/common/Zotero/storage/6XFLLTKL/Chabert-Liddell et al. - 2021 - A Stochastic Block Model Approach for the Analysis of Multilevel Networks an Application to the Soc.pdf;/home/louis/snap/zotero-snap/common/Zotero/storage/8DAMTVD8/1910.html}
}

@software{chiquetSbmStochasticBlockmodels2024,
  title = {Sbm: {{Stochastic Blockmodels}}},
  shorttitle = {Sbm},
  author = {Chiquet, Julien and Donnet, Sophie and Barbillon, Pierre},
  date = {2024-09-16},
  url = {https://cran.r-project.org/web/packages/sbm/index.html},
  urldate = {2024-11-04},
  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{$>$}.},
  version = {0.4.7}
}

@article{clausetHierarchicalStructurePrediction2008,
  title = {Hierarchical Structure and the Prediction of Missing Links in Networks},
  author = {Clauset, Aaron and Moore, Cristopher and Newman, M. E. J.},
  date = {2008-05},
  journaltitle = {Nature},
  volume = {453},
  number = {7191},
  pages = {98--101},
  publisher = {Nature Publishing Group},
  issn = {1476-4687},
  doi = {10.1038/nature06830},
  url = {https://www.nature.com/articles/nature06830},
  urldate = {2025-09-19},
  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.},
  langid = {english},
  keywords = {/unread,Humanities and Social Sciences,multidisciplinary,Science},
  annotation = {Read\_Status: New\\
Read\_Status\_Date: 2025-09-19T12:33:29.962Z},
  file = {/home/louis/snap/zotero-snap/common/Zotero/storage/Y4FR2F3U/Clauset et al. - 2008 - Hierarchical structure and the prediction of missing links in networks.pdf}
}

@incollection{ClusteringLargeApplications1990,
  title = {Clustering {{Large Applications}} ({{Program CLARA}})},
  booktitle = {Finding {{Groups}} in {{Data}}},
  date = {1990},
  pages = {126--163},
  publisher = {John Wiley \& Sons, Ltd},
  doi = {10.1002/9780470316801.ch3},
  url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/9780470316801.ch3},
  urldate = {2024-09-13},
  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},
  isbn = {978-0-470-31680-1},
  langid = {english},
  keywords = {clustering large applications,computation time,data sets,euclidean distance,interactive session},
  file = {/home/louis/snap/zotero-snap/common/Zotero/storage/R28XFDII/1990 - Clustering Large Applications (Program CLARA).pdf}
}

@article{corsoConnectivityNestednessBipartite2011,
  title = {Connectivity and {{Nestedness}} in {{Bipartite Networks}} from {{Community Ecology}}},
  author = {Corso, Gilberto and De Araujo, A I Levartoski and De Almeida, Adriana M},
  date = {2011-03-01},
  journaltitle = {Journal of Physics: Conference Series},
  shortjournal = {J. Phys.: Conf. Ser.},
  volume = {285},
  pages = {012009},
  issn = {1742-6596},
  doi = {10.1088/1742-6596/285/1/012009},
  url = {https://iopscience.iop.org/article/10.1088/1742-6596/285/1/012009},
  urldate = {2024-11-05},
  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.},
  langid = {english},
  file = {/home/louis/snap/zotero-snap/common/Zotero/storage/VJTV2ZT8/Corso et al. - 2011 - Connectivity and Nestedness in Bipartite Networks from Community Ecology.pdf}
}

@article{daudinMixtureModelRandom2008,
  title = {A Mixture Model for Random Graphs},
  author = {Daudin, J.-J. and Picard, F. and Robin, S.},
  date = {2008-06-01},
  journaltitle = {Statistics and Computing},
  shortjournal = {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/louis/snap/zotero-snap/common/Zotero/storage/439HK27B/Daudin et al. - 2008 - A mixture model for random graphs.pdf;/home/louis/snap/zotero-snap/common/Zotero/storage/HVVF5MNY/daudin2007.pdf.pdf}
}

@article{dempsterMaximumLikelihoodIncomplete1977,
  title = {Maximum {{Likelihood}} from {{Incomplete Data}} via the {{EM Algorithm}}},
  author = {Dempster, A. P. and Laird, N. M. and Rubin, D. B.},
  date = {1977},
  journaltitle = {Journal of the Royal Statistical Society. Series B (Methodological)},
  volume = {39},
  number = {1},
  eprint = {2984875},
  eprinttype = {jstor},
  pages = {1--38},
  publisher = {[Royal Statistical Society, Oxford University Press]},
  issn = {0035-9246},
  url = {https://www.jstor.org/stable/2984875},
  urldate = {2025-05-27},
  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.},
  keywords = {/unread},
  annotation = {Read\_Status: New\\
Read\_Status\_Date: 2025-05-27T16:20:41.925Z}
}

@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},
  shortjournal = {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/louis/snap/zotero-snap/common/Zotero/storage/3L7JALP4/Desjardins-Proulx et al. - 2017 - Ecological interactions and the Netflix problem.pdf}
}

@article{devotoUnderstandingPlanningEcological2012,
  title = {Understanding and Planning Ecological Restoration of Plant–Pollinator Networks},
  author = {Devoto, Mariano and Bailey, Sallie and Craze, Paul and Memmott, Jane},
  date = {2012},
  journaltitle = {Ecology Letters},
  volume = {15},
  number = {4},
  pages = {319--328},
  issn = {1461-0248},
  doi = {10.1111/j.1461-0248.2012.01740.x},
  url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1461-0248.2012.01740.x},
  urldate = {2024-08-20},
  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.},
  langid = {english},
  keywords = {Ecosystem function,functional complementarity,functional redundancy,pine forest,plant–animal interaction,plant–pollinator network,redundancy analysis,restoration,restoration strategy,succession},
  file = {/home/louis/snap/zotero-snap/common/Zotero/storage/XY2INESI/Devoto et al. - 2012 - Understanding and planning ecological restoration of plant–pollinator networks.pdf;/home/louis/snap/zotero-snap/common/Zotero/storage/MWCIJ5TW/j.1461-0248.2012.01740.html}
}

@incollection{DivisiveAnalysisProgram1990,
  title = {Divisive {{Analysis}} ({{Program DIANA}})},
  booktitle = {Finding {{Groups}} in {{Data}}},
  date = {1990},
  pages = {253--279},
  publisher = {John Wiley \& Sons, Ltd},
  doi = {10.1002/9780470316801.ch6},
  url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/9780470316801.ch6},
  urldate = {2024-09-13},
  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},
  isbn = {978-0-470-31680-1},
  langid = {english},
  keywords = {average dissimilarity,divisive analysis,divisive analysis algorithm,individual clusters,software packages},
  file = {/home/louis/snap/zotero-snap/common/Zotero/storage/QPT7Z6J3/1990 - Divisive Analysis (Program DIANA).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/louis/snap/zotero-snap/common/Zotero/storage/89ZXBJQP/10.1111@gcb.15474.pdf.pdf;/home/louis/snap/zotero-snap/common/Zotero/storage/IVR6RGG7/Doré et al. - 2021 - Relative effects of anthropogenic pressures, clima.pdf;/home/louis/snap/zotero-snap/common/Zotero/storage/WSJ4DV98/gcb.html}
}

@article{dormannIndicesGraphsNull2009,
  title = {Indices, {{Graphs}} and {{Null Models}}: {{Analyzing Bipartite Ecological Networks}}},
  shorttitle = {Indices, {{Graphs}} and {{Null Models}}},
  author = {Dormann, Carsten F. and Frund, Jochen and Bluthgen, Nico and Gruber, Bernd},
  date = {2009-02-27},
  journaltitle = {The Open Ecology Journal},
  shortjournal = {TOECOLJ},
  volume = {2},
  number = {1},
  pages = {7--24},
  issn = {18742130},
  doi = {10.2174/1874213000902010007},
  url = {http://benthamopen.com/ABSTRACT/TOECOLJ-2-1-7},
  urldate = {2025-09-18},
  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.},
  langid = {english},
  keywords = {/unread},
  annotation = {Read\_Status: New\\
Read\_Status\_Date: 2025-09-18T13:47:50.536Z},
  file = {/home/louis/snap/zotero-snap/common/Zotero/storage/IQWYN2GQ/Dormann et al. - 2009 - Indices, Graphs and Null Models Analyzing Bipartite Ecological Networks.pdf}
}

@article{elleUsePollinationNetworks2012,
  title = {The Use of Pollination Networks in Conservation{\textsuperscript{1}} {{This}} Article Is Part of a {{Special Issue}} Entitled “{{Pollination}} Biology Research in {{Canada}}: {{Perspectives}} on a Mutualism at Different Scales”.},
  shorttitle = {The Use of Pollination Networks in Conservation{\textsuperscript{1}} {{This}} Article Is Part of a {{Special Issue}} Entitled “{{Pollination}} Biology Research in {{Canada}}},
  author = {Elle, Elizabeth and Elwell, Sherri L. and Gielens, Grahame A.},
  date = {2012-07},
  journaltitle = {Botany},
  shortjournal = {Botany},
  volume = {90},
  number = {7},
  pages = {525--534},
  issn = {1916-2790, 1916-2804},
  doi = {10.1139/b11-111},
  url = {http://www.nrcresearchpress.com/doi/10.1139/b11-111},
  urldate = {2025-09-18},
  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.},
  langid = {english},
  keywords = {/unread},
  annotation = {Read\_Status: New\\
Read\_Status\_Date: 2025-09-18T15:39:13.968Z},
  file = {/home/louis/snap/zotero-snap/common/Zotero/storage/EVUPKFLQ/Elle et al. - 2012 - The use of pollination networks in conservation1 This article is part of a Special Issue.pdf}
}

@article{erdosRandomGraphs1959,
  title = {On Random Graphs. {{I}}.},
  author = {Erdős, P. and Rényi, A.},
  date = {1959},
  journaltitle = {Publicationes Mathematicae Debrecen},
  shortjournal = {Publ. Math. Debrecen},
  volume = {6},
  number = {3--4},
  pages = {290--297},
  issn = {00333883},
  doi = {10.5486/PMD.1959.6.3-4.12},
  url = {https://publi.math.unideb.hu/load_doi.php?pdoi=10_5486_PMD_1959_6_3_4_12},
  urldate = {2024-08-09},
  abstract = {Semantic Scholar extracted view of "On random graphs. I." by P. Erdos et al.},
  file = {/home/louis/snap/zotero-snap/common/Zotero/storage/WRSY3FZV/Erdős et Rényi - 2022 - On random graphs. I..pdf}
}

@article{fisogniSeasonalTrajectoriesPlantpollinator2022,
  title = {Seasonal Trajectories of Plant-Pollinator Interaction Networks Differ Following Phenological Mismatches along an Urbanization Gradient},
  author = {Fisogni, Alessandro and Hautekèete, Nina and Piquot, Yves and Brun, Marion and Vanappelghem, Cédric and Ohlmann, Marc and Franchomme, Magalie and Hinnewinkel, Christelle and Massol, François},
  date = {2022-10-01},
  journaltitle = {Landscape and Urban Planning},
  shortjournal = {Landscape and Urban Planning},
  volume = {226},
  pages = {104512},
  issn = {0169-2046},
  doi = {10.1016/j.landurbplan.2022.104512},
  url = {https://www.sciencedirect.com/science/article/pii/S016920462200161X},
  urldate = {2025-05-14},
  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.},
  keywords = {/unread},
  annotation = {Read\_Status: New\\
Read\_Status\_Date: 2025-05-14T20:18:00.025Z},
  file = {/home/louis/snap/zotero-snap/common/Zotero/storage/CCJWEIBD/Fisogni et al. - 2022 - Seasonal trajectories of plant-pollinator interaction networks differ following phenological mismatc.pdf;/home/louis/snap/zotero-snap/common/Zotero/storage/HMUM8AMZ/S016920462200161X.html}
}

@incollection{Frontmatter1990,
  title = {Frontmatter},
  booktitle = {Finding {{Groups}} in {{Data}}},
  date = {1990},
  pages = {i-xiv},
  publisher = {John Wiley \& Sons, Ltd},
  doi = {10.1002/9780470316801.fmatter},
  url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/9780470316801.fmatter},
  urldate = {2024-09-13},
  abstract = {The prelims comprise: Half Title Title Copyright Preface Contents},
  isbn = {978-0-470-31680-1},
  langid = {english},
  file = {/home/louis/snap/zotero-snap/common/Zotero/storage/FMSENR3S/1990 - Frontmatter.pdf}
}

@article{funkeStochasticBlockModels2019,
  title = {Stochastic Block Models: {{A}} Comparison of Variants and Inference Methods},
  shorttitle = {Stochastic Block Models},
  author = {Funke, Thorben and Becker, Till},
  date = {2019-04-23},
  journaltitle = {PLOS ONE},
  shortjournal = {PLOS ONE},
  volume = {14},
  number = {4},
  pages = {e0215296},
  publisher = {Public Library of Science},
  issn = {1932-6203},
  doi = {10.1371/journal.pone.0215296},
  url = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0215296},
  urldate = {2025-01-26},
  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.},
  langid = {english},
  keywords = {a lire,Algorithms,Community structure,Computer networks,Graphs,Hierarchical clustering,Metadata,Probability distribution,Simulated annealing},
  file = {/home/louis/snap/zotero-snap/common/Zotero/storage/C8IN9UTG/Funke et Becker - 2019 - Stochastic block models A comparison of variants and inference methods.pdf}
}

@incollection{FuzzyAnalysisProgram1990,
  title = {Fuzzy {{Analysis}} ({{Program FANNY}})},
  booktitle = {Finding {{Groups}} in {{Data}}},
  date = {1990},
  pages = {164--198},
  publisher = {John Wiley \& Sons, Ltd},
  doi = {10.1002/9780470316801.ch4},
  url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/9780470316801.ch4},
  urldate = {2024-09-13},
  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},
  isbn = {978-0-470-31680-1},
  langid = {english},
  keywords = {data set,fuzzy analysis,interactive session,membership coefficients,silhouette width},
  file = {/home/louis/snap/zotero-snap/common/Zotero/storage/8HMFB8MC/1990 - Fuzzy Analysis (Program FANNY).pdf}
}

@article{gibsonSamplingMethodInfluences2011,
  title = {Sampling Method Influences the Structure of Plant–Pollinator Networks},
  author = {Gibson, Rachel H. and Knott, Ben and Eberlein, Tim and Memmott, Jane},
  date = {2011},
  journaltitle = {Oikos},
  volume = {120},
  number = {6},
  pages = {822--831},
  issn = {1600-0706},
  doi = {10.1111/j.1600-0706.2010.18927.x},
  url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1600-0706.2010.18927.x},
  urldate = {2025-03-24},
  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.},
  langid = {english},
  keywords = {/unread},
  file = {/home/louis/snap/zotero-snap/common/Zotero/storage/BI4T5E29/Gibson et al. - 2011 - Sampling method influences the structure of plant–pollinator networks.pdf;/home/louis/snap/zotero-snap/common/Zotero/storage/Q4RH8QGE/j.1600-0706.2010.18927.html}
}

@article{govaertBlockClusteringBernoulli2008,
  title = {Block Clustering with {{Bernoulli}} Mixture Models: {{Comparison}} of Different Approaches},
  shorttitle = {Block Clustering with {{Bernoulli}} Mixture Models},
  author = {Govaert, Gérard and Nadif, Mohamed},
  date = {2008-02-20},
  journaltitle = {Computational Statistics \& Data Analysis},
  shortjournal = {Computational Statistics \& Data Analysis},
  volume = {52},
  number = {6},
  pages = {3233--3245},
  issn = {0167-9473},
  doi = {10.1016/j.csda.2007.09.007},
  url = {https://www.sciencedirect.com/science/article/pii/S0167947307003441},
  urldate = {2024-11-18},
  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.},
  keywords = {Block mixture model,Co-clustering,EM algorithm,Latent block model,Simultaneous clustering},
  file = {/home/louis/snap/zotero-snap/common/Zotero/storage/JF64S2R5/S0167947307003441.html}
}

@article{govaertClusteringBlockMixture2003,
  title = {Clustering with Block Mixture Models},
  author = {Govaert, Gérard and Nadif, Mohamed},
  date = {2003-02-01},
  journaltitle = {Pattern Recognition},
  shortjournal = {Pattern Recognition},
  series = {Biometrics},
  volume = {36},
  number = {2},
  pages = {463--473},
  issn = {0031-3203},
  doi = {10.1016/S0031-3203(02)00074-2},
  url = {https://www.sciencedirect.com/science/article/pii/S0031320302000742},
  urldate = {2024-11-04},
  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.},
  keywords = {Block CEM algorithm,Block mixture model,Clustering,EM algorithm,Latent block model,Mixture model},
  file = {/home/louis/snap/zotero-snap/common/Zotero/storage/HYHS4ZRY/S0031320302000742.html}
}

@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/louis/snap/zotero-snap/common/Zotero/storage/6IG45HH2/govaert2005.pdf.pdf;/home/louis/snap/zotero-snap/common/Zotero/storage/TL8M3XRF/Govaert et Nadif - 2005 - An EM algorithm for the block mixture model.pdf;/home/louis/snap/zotero-snap/common/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/louis/snap/zotero-snap/common/Zotero/storage/PPHP33Z9/Govaert et Nadif - 2010 - Latent Block Model for Contingency Table.pdf;/home/louis/snap/zotero-snap/common/Zotero/storage/UT8TARCX/govaert2010.pdf.pdf}
}

@software{GrossSBMColSBM2025,
  title = {{{GrossSBM}}/{{colSBM}}},
  date = {2025-07-16T10:36:01Z},
  origdate = {2021-12-30T11:52:03Z},
  url = {https://github.com/GrossSBM/colSBM},
  urldate = {2025-09-25},
  abstract = {R package for the joint stochastic blockmodeling of collection of networks},
  organization = {GroßBM},
  keywords = {/unread},
  annotation = {Read\_Status: New\\
Read\_Status\_Date: 2025-09-25T11:59:36.376Z}
}

@online{hamiltonInductiveRepresentationLearning2018,
  title = {Inductive {{Representation Learning}} on {{Large Graphs}}},
  author = {Hamilton, William L. and Ying, Rex and Leskovec, Jure},
  date = {2018-09-10},
  eprint = {1706.02216},
  eprinttype = {arXiv},
  eprintclass = {cs},
  doi = {10.48550/arXiv.1706.02216},
  url = {http://arxiv.org/abs/1706.02216},
  urldate = {2025-07-01},
  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.},
  pubstate = {prepublished},
  keywords = {Computer Science - Machine Learning,Computer Science - Social and Information Networks,Statistics - Machine Learning},
  annotation = {Read\_Status: New\\
Read\_Status\_Date: 2025-07-01T13:24:50.464Z},
  file = {/home/louis/snap/zotero-snap/common/Zotero/storage/5IKLA8LH/Hamilton et al. - 2018 - Inductive Representation Learning on Large Graphs.pdf;/home/louis/snap/zotero-snap/common/Zotero/storage/4X2VY4XN/1706.html}
}

@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/louis/snap/zotero-snap/common/Zotero/storage/7UYRBBA2/Hoff et al. - 2002 - Latent Space Approaches to Social Network Analysis.pdf;/home/louis/snap/zotero-snap/common/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},
  shortjournal = {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/louis/snap/zotero-snap/common/Zotero/storage/6F8YT8AD/holland1983.pdf.pdf;/home/louis/snap/zotero-snap/common/Zotero/storage/7DSZ3KD9/Holland et al. - 1983 - Stochastic blockmodels First steps.pdf;/home/louis/snap/zotero-snap/common/Zotero/storage/DUL2RV8Q/holland1983.pdf.pdf;/home/louis/snap/zotero-snap/common/Zotero/storage/G9KZBG9W/0378873383900217.html}
}

@article{hubertComparingPartitions1985,
  title = {Comparing Partitions},
  author = {Hubert, Lawrence and Arabie, Phipps},
  date = {1985-12-01},
  journaltitle = {Journal of Classification},
  shortjournal = {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/louis/snap/zotero-snap/common/Zotero/storage/7TKW7HEM/Hubert et Arabie - 1985 - Comparing partitions.pdf}
}

@incollection{Introduction1990,
  title = {Introduction},
  booktitle = {Finding {{Groups}} in {{Data}}},
  date = {1990},
  pages = {1--67},
  publisher = {John Wiley \& Sons, Ltd},
  doi = {10.1002/9780470316801.ch1},
  url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/9780470316801.ch1},
  urldate = {2024-09-13},
  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},
  isbn = {978-0-470-31680-1},
  langid = {english},
  keywords = {archeological findings,cluster analysis,interval-scaled variables,social sciences,spherical clusters},
  file = {/home/louis/snap/zotero-snap/common/Zotero/storage/ZPWRCT6C/1990 - Introduction.pdf}
}

@article{karrerStochasticBlockmodelsCommunity2011,
  title = {Stochastic Blockmodels and Community Structure in Networks},
  author = {Karrer, Brian and Newman, M. E. J.},
  date = {2011-01-21},
  journaltitle = {Physical Review E},
  shortjournal = {Phys. Rev. E},
  volume = {83},
  number = {1},
  eprint = {1008.3926},
  eprinttype = {arXiv},
  eprintclass = {physics},
  pages = {016107},
  issn = {1539-3755, 1550-2376},
  doi = {10.1103/PhysRevE.83.016107},
  url = {http://arxiv.org/abs/1008.3926},
  urldate = {2025-09-26},
  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.},
  langid = {english},
  keywords = {/unread,Computer Science - Social and Information Networks,Condensed Matter - Statistical Mechanics,Physics - Data Analysis Statistics and Probability,Physics - Physics and Society},
  annotation = {Read\_Status: New\\
Read\_Status\_Date: 2025-09-26T08:18:22.155Z},
  file = {/home/louis/snap/zotero-snap/common/Zotero/storage/HDPN46QR/Karrer et Newman - 2011 - Stochastic blockmodels and community structure in networks.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/louis/snap/zotero-snap/common/Zotero/storage/VXVQ5CPH/Kaszewska-Gilas et al. - 2021 - Global Studies of the Host-Parasite Relationships .pdf}
}

@book{kaufmanFindingGroupsData1990,
  title = {Finding {{Groups}} in {{Data}}: {{An Introduction}} to {{Cluster Analysis}}},
  shorttitle = {Finding {{Groups}} in {{Data}}},
  author = {Kaufman, Leonard and Rousseeuw, Peter J.},
  date = {1990-03-08},
  series = {Wiley {{Series}} in {{Probability}} and {{Statistics}}},
  edition = {1},
  publisher = {Wiley},
  doi = {10.1002/9780470316801},
  url = {https://onlinelibrary.wiley.com/doi/book/10.1002/9780470316801},
  urldate = {2024-09-13},
  isbn = {978-0-471-87876-6 978-0-470-31680-1},
  langid = {english},
  file = {/home/louis/snap/zotero-snap/common/Zotero/storage/HTL6RWZ7/Kaufman et Rousseeuw - 1990 - Finding Groups in Data An Introduction to Cluster Analysis.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 = {Statistics and Computing},
  shortjournal = {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/louis/snap/zotero-snap/common/Zotero/storage/49IKUHMA/s11222-014-9472-2.pdf.pdf;/home/louis/snap/zotero-snap/common/Zotero/storage/VXKAK359/Keribin et al. - 2015 - Estimation and selection for the latent block mode.pdf}
}

@article{kernighanEfficientHeuristicProcedure1970,
  title = {An Efficient Heuristic Procedure for Partitioning Graphs},
  author = {Kernighan, B. W. and Lin, S.},
  date = {1970-02},
  journaltitle = {The Bell System Technical Journal},
  volume = {49},
  number = {2},
  pages = {291--307},
  issn = {0005-8580},
  doi = {10.1002/j.1538-7305.1970.tb01770.x},
  url = {https://ieeexplore.ieee.org/abstract/document/6771089},
  urldate = {2025-01-26},
  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.},
  eventtitle = {The {{Bell System Technical Journal}}},
  keywords = {a lire},
  file = {/home/louis/snap/zotero-snap/common/Zotero/storage/W2RM4C9T/6771089.html}
}

@online{kipfVariationalGraphAutoEncoders2016a,
  title = {Variational {{Graph Auto-Encoders}}},
  author = {Kipf, Thomas N. and Welling, Max},
  date = {2016-11-21},
  eprint = {1611.07308},
  eprinttype = {arXiv},
  eprintclass = {stat},
  doi = {10.48550/arXiv.1611.07308},
  url = {http://arxiv.org/abs/1611.07308},
  urldate = {2025-05-09},
  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 = {prepublished},
  keywords = {Computer Science - Machine Learning,Statistics - Machine Learning},
  annotation = {Read\_Status: New\\
Read\_Status\_Date: 2025-05-09T11:54:37.094Z},
  file = {/home/louis/snap/zotero-snap/common/Zotero/storage/5THEWLW6/Kipf et Welling - 2016 - Variational Graph Auto-Encoders.pdf;/home/louis/snap/zotero-snap/common/Zotero/storage/BBTHQNRZ/1611.html}
}

@book{kolaczykStatisticalAnalysisNetwork2009,
  title = {Statistical {{Analysis}} of {{Network Data}}: {{Methods}} and {{Models}}},
  shorttitle = {Statistical {{Analysis}} of {{Network Data}}},
  author = {Kolaczyk, Eric D.},
  date = {2009},
  series = {Springer {{Series}} in {{Statistics}}},
  publisher = {Springer New York},
  location = {New York, NY},
  doi = {10.1007/978-0-387-88146-1},
  url = {https://link.springer.com/10.1007/978-0-387-88146-1},
  urldate = {2025-05-26},
  isbn = {978-0-387-88145-4 978-0-387-88146-1},
  langid = {english},
  keywords = {/unread},
  annotation = {Read\_Status: New\\
Read\_Status\_Date: 2025-05-26T11:42:27.939Z},
  file = {/home/louis/snap/zotero-snap/common/Zotero/storage/RQPMHFGB/Kolaczyk - 2009 - Statistical Analysis of Network Data Methods and Models.pdf}
}

@online{kumpulainenYourBlockOur2024,
  title = {From Your {{Block}} to Our {{Block}}: {{How}} to {{Find Shared Structure}} between {{Stochastic Block Models}} over {{Multiple Graphs}}},
  shorttitle = {From Your {{Block}} to Our {{Block}}},
  author = {Kumpulainen, Iiro and Dalleiger, Sebastian and Vreeken, Jilles and Tatti, Nikolaj},
  date = {2024-12-20},
  eprint = {2412.15476},
  eprinttype = {arXiv},
  eprintclass = {cs},
  doi = {10.48550/arXiv.2412.15476},
  url = {http://arxiv.org/abs/2412.15476},
  urldate = {2025-01-09},
  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.},
  langid = {english},
  pubstate = {prepublished},
  keywords = {Computer Science - Social and Information Networks},
  file = {/home/louis/snap/zotero-snap/common/Zotero/storage/R9S2BRF7/Kumpulainen et al. - 2024 - From your Block to our Block How to Find Shared Structure between Stochastic Block Models over Mult.pdf}
}

@inproceedings{kunegisLinkPredictionProblem2010,
  title = {The {{Link Prediction Problem}} in {{Bipartite Networks}}},
  booktitle = {Computational {{Intelligence}} for {{Knowledge-Based Systems Design}}},
  author = {Kunegis, Jérôme and De Luca, Ernesto W. and Albayrak, Sahin},
  editor = {Hüllermeier, Eyke and Kruse, Rudolf and Hoffmann, Frank},
  date = {2010},
  pages = {380--389},
  publisher = {Springer},
  location = {Berlin, Heidelberg},
  doi = {10.1007/978-3-642-14049-5_39},
  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.},
  isbn = {978-3-642-14049-5},
  langid = {english},
  keywords = {/unread,Bipartite Graph,Bipartite Network,Link Prediction,Mean Average Precision,Preferential Attachment},
  file = {/home/louis/snap/zotero-snap/common/Zotero/storage/9JHHFCDM/Kunegis et al. - 2010 - The Link Prediction Problem in Bipartite Networks.pdf}
}

@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/louis/snap/zotero-snap/common/Zotero/storage/MA2VH6NX/9503.html}
}

@article{latoucheVariationalBayesianInference2012,
  title = {Variational {{Bayesian}} Inference and Complexity Control for Stochastic Block Models},
  author = {Latouche, P and Birmelé, E and Ambroise, C},
  date = {2012-02-01},
  journaltitle = {Statistical Modelling},
  volume = {12},
  number = {1},
  pages = {93--115},
  publisher = {SAGE Publications India},
  issn = {1471-082X},
  doi = {10.1177/1471082X1001200105},
  url = {https://doi.org/10.1177/1471082X1001200105},
  urldate = {2025-01-26},
  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.},
  langid = {english},
  keywords = {a lire},
  file = {/home/louis/snap/zotero-snap/common/Zotero/storage/AWA4CBIX/Latouche et al. - 2012 - Variational Bayesian inference and complexity control for stochastic block models.pdf}
}

@software{legerBlockmodelsLatentStochastic2021,
  title = {Blockmodels: {{Latent}} and {{Stochastic Block Model Estimation}} by a '{{V-EM}}' {{Algorithm}}},
  shorttitle = {Blockmodels},
  author = {Leger, Jean-Benoist and Barbillon, Pierre and Chiquet, Julien},
  date = {2021-12-01},
  url = {https://cran.r-project.org/web/packages/blockmodels/index.html},
  urldate = {2024-11-04},
  abstract = {Latent and Stochastic Block Model estimation by a Variational EM algorithm. Various probability distribution are provided (Bernoulli, Poisson...), with or without covariates.},
  version = {1.1.5}
}

@article{llopis-belenguerSensitivityBipartiteNetwork2023,
  title = {Sensitivity of Bipartite Network Analyses to Incomplete Sampling and Taxonomic Uncertainty},
  author = {Llopis-Belenguer, Cristina and Balbuena, Juan Antonio and Blasco-Costa, Isabel and Karvonen, Anssi and Sarabeev, Volodimir and Jokela, Jukka},
  date = {2023-04},
  journaltitle = {Ecology},
  volume = {104},
  number = {4},
  pages = {e3974},
  publisher = {John Wiley \& Sons, Ltd},
  issn = {0012-9658},
  doi = {10.1002/ecy.3974},
  url = {https://esajournals.onlinelibrary.wiley.com/doi/full/10.1002/ecy.3974},
  urldate = {2025-09-18},
  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 \textasciitilde 76\% and \textasciitilde 12\%, respectively, and nestedness and connectance increased by \textasciitilde 114\% and \textasciitilde 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 (\textasciitilde 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.},
  keywords = {/unread,bipartite networks,host–parasite interactions,sampling completeness,sampling issues,taxonomic resolution},
  annotation = {Read\_Status: New\\
Read\_Status\_Date: 2025-09-18T14:55:12.700Z},
  file = {/home/louis/snap/zotero-snap/common/Zotero/storage/XC8KSS5S/Llopis-Belenguer et al. - 2023 - Sensitivity of bipartite network analyses to incomplete sampling and taxonomic uncertainty.pdf}
}

@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{matiasStatisticalClusteringTemporal2017,
  title = {Statistical {{Clustering}} of {{Temporal Networks Through}} a {{Dynamic Stochastic Block Model}}},
  author = {Matias, Catherine and Miele, Vincent},
  date = {2017-09-01},
  journaltitle = {Journal of the Royal Statistical Society Series B: Statistical Methodology},
  shortjournal = {J. R. Stat. Soc. Ser. B. Stat. Methodol.},
  volume = {79},
  number = {4},
  pages = {1119--1141},
  issn = {1369-7412},
  doi = {10.1111/rssb.12200},
  url = {https://doi.org/10.1111/rssb.12200},
  urldate = {2025-09-19},
  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.},
  keywords = {/unread},
  annotation = {Read\_Status: New\\
Read\_Status\_Date: 2025-09-19T14:13:35.038Z},
  file = {/home/louis/snap/zotero-snap/common/Zotero/storage/DHG8XRWV/Matias et Miele - 2017 - Statistical Clustering of Temporal Networks Through a Dynamic Stochastic Block Model.pdf;/home/louis/snap/zotero-snap/common/Zotero/storage/4YPTDS8N/rssb.html}
}

@article{michalska-smithTellingEcologicalNetworks2019,
  title = {Telling Ecological Networks Apart by Their Structure: {{A}} Computational Challenge},
  shorttitle = {Telling Ecological Networks Apart by Their Structure},
  author = {Michalska-Smith, Matthew J. and Allesina, Stefano},
  editor = {Bollenbach, Tobias},
  date = {2019-06-27},
  journaltitle = {PLOS Computational Biology},
  shortjournal = {PLoS Comput Biol},
  volume = {15},
  number = {6},
  pages = {e1007076},
  issn = {1553-7358},
  doi = {10.1371/journal.pcbi.1007076},
  url = {https://dx.plos.org/10.1371/journal.pcbi.1007076},
  urldate = {2025-04-11},
  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.},
  langid = {english},
  keywords = {/unread},
  file = {/home/louis/snap/zotero-snap/common/Zotero/storage/4UCENIQQ/Michalska-Smith et Allesina - 2019 - Telling ecological networks apart by their structure A computational challenge.pdf}
}

@incollection{MonotheticAnalysisProgram1990,
  title = {Monothetic {{Analysis}} ({{Program MONA}})},
  booktitle = {Finding {{Groups}} in {{Data}}},
  date = {1990},
  pages = {280--311},
  publisher = {John Wiley \& Sons, Ltd},
  doi = {10.1002/9780470316801.ch7},
  url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/9780470316801.ch7},
  urldate = {2024-09-13},
  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},
  isbn = {978-0-470-31680-1},
  langid = {english},
  keywords = {binary variables,chimpanzee,dissimilarity matrix,missing measurements,monothetic analysis},
  file = {/home/louis/snap/zotero-snap/common/Zotero/storage/UU46RRAJ/1990 - Monothetic Analysis (Program MONA).pdf}
}

@inproceedings{ngSpectralClusteringAnalysis2001,
  title = {On {{Spectral Clustering}}: {{Analysis}} and an Algorithm},
  shorttitle = {On {{Spectral Clustering}}},
  booktitle = {Advances in {{Neural Information Processing Systems}}},
  author = {Ng, Andrew and Jordan, Michael and Weiss, Yair},
  date = {2001},
  volume = {14},
  publisher = {MIT Press},
  url = {https://papers.nips.cc/paper_files/paper/2001/hash/801272ee79cfde7fa5960571fee36b9b-Abstract.html},
  urldate = {2025-10-08},
  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.},
  annotation = {Read\_Status: New\\
Read\_Status\_Date: 2025-10-08T12:47:34.666Z},
  file = {/home/louis/snap/zotero-snap/common/Zotero/storage/SEWUWLHD/Ng et al. - 2001 - On Spectral Clustering Analysis and an algorithm.pdf;/home/louis/snap/zotero-snap/common/Zotero/storage/XRY4F47W/nips01-spectral.pdf}
}

@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/louis/snap/zotero-snap/common/Zotero/storage/DFN9BYBR/28045499.html}
}

@incollection{PartitioningMedoidsProgram1990,
  title = {Partitioning {{Around Medoids}} ({{Program PAM}})},
  booktitle = {Finding {{Groups}} in {{Data}}},
  date = {1990},
  pages = {68--125},
  publisher = {John Wiley \& Sons, Ltd},
  doi = {10.1002/9780470316801.ch2},
  url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/9780470316801.ch2},
  urldate = {2024-09-13},
  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},
  isbn = {978-0-470-31680-1},
  langid = {english},
  keywords = {central memory,graphical representation,medoids,partitioning around medoids,representative objects},
  file = {/home/louis/snap/zotero-snap/common/Zotero/storage/8MSBVNEH/1990 - Partitioning Around Medoids (Program PAM).pdf}
}

@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},
  shortjournal = {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/louis/snap/zotero-snap/common/Zotero/storage/2KJFL3SB/Pavlopoulos et al. - 2018 - Bipartite graphs in systems biology and medicine .pdf;/home/louis/snap/zotero-snap/common/Zotero/storage/A2Y2EGPA/pavlopoulos2018.pdf.pdf;/home/louis/snap/zotero-snap/common/Zotero/storage/UK2MK5FW/pavlopoulos2018.pdf.pdf;/home/louis/snap/zotero-snap/common/Zotero/storage/XP7G4PZF/4875933.html}
}

@article{pavlopoulosBipartiteGraphsSystems2018a,
  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://academic.oup.com/gigascience/article/doi/10.1093/gigascience/giy014/4875933},
  urldate = {2025-04-10},
  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.},
  langid = {english},
  keywords = {/unread},
  file = {/home/louis/snap/zotero-snap/common/Zotero/storage/YP235FFE/Pavlopoulos et al. - 2018 - Bipartite graphs in systems biology and medicine a survey of methods and applications.pdf}
}

@article{pavlovicMultisubjectStochasticBlockmodels2020,
  title = {Multi-Subject {{Stochastic Blockmodels}} for Adaptive Analysis of Individual Differences in Human Brain Network Cluster Structure},
  author = {Pavlović, Dragana M. and Guillaume, Bryan R. L. and Towlson, Emma K. and Kuek, Nicole M. Y. and Afyouni, Soroosh and Vértes, Petra E. and Yeo, B. T. Thomas and Bullmore, Edward T. and Nichols, Thomas E.},
  date = {2020-10-15},
  journaltitle = {NeuroImage},
  shortjournal = {Neuroimage},
  volume = {220},
  eprint = {32058004},
  eprinttype = {pubmed},
  pages = {116611},
  issn = {1095-9572},
  doi = {10.1016/j.neuroimage.2020.116611},
  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.},
  langid = {english},
  keywords = {/unread,Brain,Community detection,Computer Simulation,Connectome,Default Mode Network,Firth estimation,Humans,Individuality,Integrated classification likelihood criterion,Likelihood ratio,Magnetic Resonance Imaging,Mixture models,Models Neurological,Models Statistical,Modularity,Multi-subject network analysis,Nerve Net,Network analysis,Permutation test,Schizophrenia,Stochastic block model,Stochastic blockmodel,Variational approximation,Wald test},
  annotation = {Read\_Status: New\\
Read\_Status\_Date: 2025-09-19T14:04:30.797Z},
  file = {/home/louis/snap/zotero-snap/common/Zotero/storage/ENZLGULT/Pavlović et al. - 2020 - Multi-subject Stochastic Blockmodels for adaptive analysis of individual differences in human brain.pdf}
}

@online{peixotoBayesianStochasticBlockmodeling2023,
  title = {Bayesian Stochastic Blockmodeling},
  author = {Peixoto, Tiago P.},
  date = {2023-03-22},
  eprint = {1705.10225},
  eprinttype = {arXiv},
  eprintclass = {stat},
  doi = {10.1002/9781119483298.ch11},
  url = {http://arxiv.org/abs/1705.10225},
  urldate = {2025-01-26},
  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.},
  langid = {english},
  pubstate = {prepublished},
  keywords = {a lire,Condensed Matter - Statistical Mechanics,Physics - Data Analysis Statistics and Probability,Statistics - Machine Learning},
  file = {/home/louis/snap/zotero-snap/common/Zotero/storage/L34VRKGF/Peixoto - 2023 - Bayesian stochastic blockmodeling.pdf}
}

@article{peixotoEfficientMonteCarlo2014,
  title = {Efficient {{Monte Carlo}} and Greedy Heuristic for the Inference of Stochastic Block Models},
  author = {Peixoto, Tiago P.},
  date = {2014-01-13},
  journaltitle = {Physical Review E},
  shortjournal = {Phys. Rev. E},
  volume = {89},
  number = {1},
  pages = {012804},
  publisher = {American Physical Society},
  doi = {10.1103/PhysRevE.89.012804},
  url = {https://link.aps.org/doi/10.1103/PhysRevE.89.012804},
  urldate = {2025-01-26},
  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⁡(𝑁⁢ln2⁡𝑁) 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.},
  keywords = {a lire},
  file = {/home/louis/snap/zotero-snap/common/Zotero/storage/9C6TE4FS/Peixoto - 2014 - Efficient Monte Carlo and greedy heuristic for the inference of stochastic block models.pdf;/home/louis/snap/zotero-snap/common/Zotero/storage/Q4LRS4GA/PhysRevE.89.html}
}

@article{peixotoHierarchicalBlockStructures2014,
  title = {Hierarchical {{Block Structures}} and {{High-Resolution Model Selection}} in {{Large Networks}}},
  author = {Peixoto, Tiago P.},
  date = {2014-03-24},
  journaltitle = {Physical Review X},
  shortjournal = {Phys. Rev. X},
  volume = {4},
  number = {1},
  pages = {011047},
  issn = {2160-3308},
  doi = {10.1103/PhysRevX.4.011047},
  url = {https://link.aps.org/doi/10.1103/PhysRevX.4.011047},
  urldate = {2025-09-26},
  langid = {english},
  keywords = {/unread},
  annotation = {Read\_Status: New\\
Read\_Status\_Date: 2025-09-26T08:27:38.586Z},
  file = {/home/louis/snap/zotero-snap/common/Zotero/storage/R58ZQK8J/Peixoto - 2014 - Hierarchical Block Structures and High-Resolution Model Selection in Large Networks.pdf}
}

@article{pichonTellingMutualisticAntagonistic2024,
  title = {Telling Mutualistic and Antagonistic Ecological Networks Apart by Learning Their Multiscale Structure},
  author = {Pichon, Benoît and Le Goff, Rémy and Morlon, Hélène and Perez-Lamarque, Benoît},
  date = {2024},
  journaltitle = {Methods in Ecology and Evolution},
  volume = {15},
  number = {6},
  pages = {1113--1128},
  issn = {2041-210X},
  doi = {10.1111/2041-210X.14328},
  url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/2041-210X.14328},
  urldate = {2024-06-17},
  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.},
  langid = {english},
  keywords = {ecological interactions,interaction classification,machine learning,motif frequency,network structure},
  file = {/home/louis/snap/zotero-snap/common/Zotero/storage/9DFZFNV7/Pichon et al. - 2024 - Telling mutualistic and antagonistic ecological ne.pdf;/home/louis/snap/zotero-snap/common/Zotero/storage/RZXQ6LCV/2041-210X.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},
  shortjournal = {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/louis/snap/zotero-snap/common/Zotero/storage/ATY3ZP2X/Ramos-Jiliberto et al. - 2010 - Topological change of Andean plant–pollinator netw.pdf;/home/louis/snap/zotero-snap/common/Zotero/storage/HPBGUP65/ramos-jiliberto2010.pdf.pdf;/home/louis/snap/zotero-snap/common/Zotero/storage/I33MZQQ7/ramos-jiliberto2010.pdf.pdf;/home/louis/snap/zotero-snap/common/Zotero/storage/YJX8XBNW/S1476945X09000622.html}
}

@online{rebafkaModelbasedClusteringMultiple2023,
  title = {Model-Based Clustering of Multiple Networks with a Hierarchical Algorithm},
  author = {Rebafka, Tabea},
  date = {2023-11-06},
  eprint = {2211.02314},
  eprinttype = {arXiv},
  eprintclass = {math, stat},
  doi = {10.48550/arXiv.2211.02314},
  url = {http://arxiv.org/abs/2211.02314},
  urldate = {2024-07-22},
  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.},
  pubstate = {prepublished},
  keywords = {Mathematics - Statistics Theory},
  file = {/home/louis/snap/zotero-snap/common/Zotero/storage/B9C8S8WQ/Rebafka - 2023 - Model-based clustering of multiple networks with a.pdf;/home/louis/snap/zotero-snap/common/Zotero/storage/GG7C6CNM/2211.html}
}

@incollection{References1990,
  title = {References},
  booktitle = {Finding {{Groups}} in {{Data}}},
  date = {1990},
  pages = {320--331},
  publisher = {John Wiley \& Sons, Ltd},
  doi = {10.1002/9780470316801.refs},
  url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/9780470316801.refs},
  urldate = {2024-09-13},
  isbn = {978-0-470-31680-1},
  langid = {english},
  file = {/home/louis/snap/zotero-snap/common/Zotero/storage/YKP9NU4L/1990 - References.pdf}
}

@article{rivera-hutinelEffectsSamplingCompleteness2012,
  title = {Effects of Sampling Completeness on the Structure of Plant–Pollinator Networks},
  author = {Rivera-Hutinel, A. and Bustamante, R. O. and Marín, V. H. and Medel, R.},
  date = {2012},
  journaltitle = {Ecology},
  volume = {93},
  number = {7},
  pages = {1593--1603},
  issn = {1939-9170},
  doi = {10.1890/11-1803.1},
  url = {https://onlinelibrary.wiley.com/doi/abs/10.1890/11-1803.1},
  urldate = {2025-09-18},
  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.},
  langid = {english},
  keywords = {/unread,accumulation curves,Chile,Clench model,ecological networks,Los Ruiles National Reserve,network size,plant–pollinator network metrics,sampling completeness,sampling effort,sampling evenness},
  annotation = {Read\_Status: New\\
Read\_Status\_Date: 2025-09-18T15:47:50.369Z},
  file = {/home/louis/snap/zotero-snap/common/Zotero/storage/PJ7NWTIV/Rivera-Hutinel et al. - 2012 - Effects of sampling completeness on the structure of plant–pollinator networks.pdf;/home/louis/snap/zotero-snap/common/Zotero/storage/37GFBFZH/11-1803.html}
}

@article{sanderWhatCanInteraction2015,
  title = {What {{Can Interaction Webs Tell Us About Species Roles}}?},
  author = {Sander, Elizabeth L. and Wootton, J. Timothy and Allesina, Stefano},
  date = {2015-07-21},
  journaltitle = {PLoS Computational Biology},
  volume = {11},
  number = {7},
  eprint = {26197151},
  eprinttype = {pubmed},
  pages = {e1004330},
  doi = {10.1371/journal.pcbi.1004330},
  url = {https://pmc.ncbi.nlm.nih.gov/articles/PMC4511233/},
  urldate = {2024-11-04},
  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 ...},
  langid = {english},
  file = {/home/louis/snap/zotero-snap/common/Zotero/storage/ZMBEJAED/Sander et al. - 2015 - What Can Interaction Webs Tell Us About Species Roles.pdf}
}

@article{schwarzEstimatingDimensionModel1978,
  title = {Estimating the {{Dimension}} of a {{Model}}},
  author = {Schwarz, Gideon},
  date = {1978-03},
  journaltitle = {The Annals of Statistics},
  volume = {6},
  number = {2},
  pages = {461--464},
  publisher = {Institute of Mathematical Statistics},
  issn = {0090-5364, 2168-8966},
  doi = {10.1214/aos/1176344136},
  url = {https://projecteuclid.org/journals/annals-of-statistics/volume-6/issue-2/Estimating-the-Dimension-of-a-Model/10.1214/aos/1176344136.full},
  urldate = {2025-01-29},
  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.},
  keywords = {62F99,62J99,Akaike information criterion,asymptotics,dimension},
  file = {/home/louis/snap/zotero-snap/common/Zotero/storage/L8CXITBN/Schwarz - 1978 - Estimating the Dimension of a Model.pdf}
}

@article{sheykhaliRobustnessExtinctionPlasticity2020,
  title = {Robustness to Extinction and Plasticity Derived from Mutualistic Bipartite Ecological Networks},
  author = {Sheykhali, Somaye and Fernández-Gracia, Juan and Traveset, Anna and Ziegler, Maren and Voolstra, Christian R. and Duarte, Carlos M. and Eguíluz, Víctor M.},
  date = {2020-06-17},
  journaltitle = {Scientific Reports},
  shortjournal = {Sci Rep},
  volume = {10},
  number = {1},
  pages = {9783},
  publisher = {Nature Publishing Group},
  issn = {2045-2322},
  doi = {10.1038/s41598-020-66131-5},
  url = {https://www.nature.com/articles/s41598-020-66131-5},
  urldate = {2025-09-18},
  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.},
  langid = {english},
  keywords = {/unread,Biodiversity,Complex networks,Ecological networks},
  annotation = {Read\_Status: New\\
Read\_Status\_Date: 2025-09-18T14:43:11.755Z},
  file = {/home/louis/snap/zotero-snap/common/Zotero/storage/YVCBAJYI/Sheykhali et al. - 2020 - Robustness to extinction and plasticity derived from mutualistic bipartite ecological networks.pdf}
}

@article{simmonsMotifsBipartiteEcological2019,
  title = {Motifs in Bipartite Ecological Networks: Uncovering Indirect Interactions},
  shorttitle = {Motifs in Bipartite Ecological Networks},
  author = {Simmons, Benno I. and Cirtwill, Alyssa R. and Baker, Nick J. and Wauchope, Hannah S. and Dicks, Lynn V. and Stouffer, Daniel B. and Sutherland, William J.},
  date = {2019-01},
  journaltitle = {Oikos},
  shortjournal = {Oikos},
  volume = {128},
  number = {2},
  pages = {154--170},
  issn = {0030-1299, 1600-0706},
  doi = {10.1111/oik.05670},
  url = {https://onlinelibrary.wiley.com/doi/10.1111/oik.05670},
  urldate = {2025-04-10},
  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.},
  langid = {english},
  keywords = {/unread},
  file = {/home/louis/snap/zotero-snap/common/Zotero/storage/3BFRNIK2/Simmons et al. - 2019 - Motifs in bipartite ecological networks uncovering indirect interactions.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 = {Journal of Classification},
  shortjournal = {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/louis/snap/zotero-snap/common/Zotero/storage/2GYRASW5/snijders1997.pdf.pdf;/home/louis/snap/zotero-snap/common/Zotero/storage/JJNQV32Y/Snijders et Nowicki - 1997 - Estimation and Prediction for Stochastic Blockmode.pdf;/home/louis/snap/zotero-snap/common/Zotero/storage/LXGG9SRP/snijders1997.pdf.pdf}
}

@article{souzaTemporalVariationPlant2018,
  title = {Temporal Variation in Plant–Pollinator Networks from Seasonal Tropical Environments: {{Higher}} Specialization When Resources Are Scarce},
  shorttitle = {Temporal Variation in Plant–Pollinator Networks from Seasonal Tropical Environments},
  author = {Souza, Camila S. and Maruyama, Pietro K. and Aoki, Camila and Sigrist, Maria R. and Raizer, Josué and Gross, Caroline L. and family=Araujo, given=Andréa C., prefix=de, useprefix=true},
  date = {2018},
  journaltitle = {Journal of Ecology},
  volume = {106},
  number = {6},
  pages = {2409--2420},
  issn = {1365-2745},
  doi = {10.1111/1365-2745.12978},
  url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/1365-2745.12978},
  urldate = {2025-03-24},
  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.},
  langid = {english},
  keywords = {/unread,Cerrado,functional diversity,modularity,nestedness,network sampling,Pantanal,resource availability,seasonality},
  file = {/home/louis/snap/zotero-snap/common/Zotero/storage/ABKWLF45/Souza et al. - 2018 - Temporal variation in plant–pollinator networks from seasonal tropical environments Higher speciali.pdf;/home/louis/snap/zotero-snap/common/Zotero/storage/KGWHZ5H2/1365-2745.html}
}

@incollection{SubjectIndex1990,
  title = {Subject {{Index}}},
  booktitle = {Finding {{Groups}} in {{Data}}},
  date = {1990},
  pages = {335--342},
  publisher = {John Wiley \& Sons, Ltd},
  doi = {10.1002/9780470316801.indsub},
  url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/9780470316801.indsub},
  urldate = {2024-09-13},
  isbn = {978-0-470-31680-1},
  langid = {english},
  file = {/home/louis/snap/zotero-snap/common/Zotero/storage/FWYM943T/1990 - Subject Index.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}
}

@article{trojelsgaardMacroecologyPollinationNetworks2013,
  title = {Macroecology of Pollination Networks},
  author = {Trøjelsgaard, Kristian and Olesen, Jens M.},
  date = {2013},
  journaltitle = {Global Ecology and Biogeography},
  volume = {22},
  number = {2},
  pages = {149--162},
  issn = {1466-8238},
  doi = {10.1111/j.1466-8238.2012.00777.x},
  url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1466-8238.2012.00777.x},
  urldate = {2025-03-24},
  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.},
  langid = {english},
  keywords = {/unread,Climate change,ecological networks,geographical gradients,macroecology,pollination,sampling effort,species interactions},
  file = {/home/louis/snap/zotero-snap/common/Zotero/storage/P6MW22VI/Trøjelsgaard et Olesen - 2013 - Macroecology of pollination networks.pdf;/home/louis/snap/zotero-snap/common/Zotero/storage/JKFY35H2/j.1466-8238.2012.00777.html}
}

@online{WebLifeEcological2022,
  title = {Web of {{Life}}: Ecological Networks Database},
  date = {2022-07},
  url = {https://www.web-of-life.es/map.php},
  urldate = {2023-06-17},
  keywords = {networks,site},
  file = {/home/louis/snap/zotero-snap/common/Zotero/storage/9WZE8QLQ/map.html}
}

@article{willsMetricsGraphComparison2020a,
  title = {Metrics for Graph Comparison: {{A}} Practitioner’s Guide},
  shorttitle = {Metrics for Graph Comparison},
  author = {Wills, Peter and Meyer, François G.},
  date = {2020-02-12},
  journaltitle = {PLOS ONE},
  shortjournal = {PLOS ONE},
  volume = {15},
  number = {2},
  pages = {e0228728},
  publisher = {Public Library of Science},
  issn = {1932-6203},
  doi = {10.1371/journal.pone.0228728},
  url = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0228728},
  urldate = {2025-09-18},
  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.},
  langid = {english},
  keywords = {/unread,Community structure,Connectomics,Distance measurement,Eigenvalues,Mathematical models,Neural networks,Random graphs,Schools},
  annotation = {Read\_Status: New\\
Read\_Status\_Date: 2025-09-18T14:47:55.289Z},
  file = {/home/louis/snap/zotero-snap/common/Zotero/storage/ZBX75DTN/Wills et Meyer - 2020 - Metrics for graph comparison A practitioner’s guide.pdf}
}

@article{xuUnderstandingGraphEmbedding2021,
  title = {Understanding {{Graph Embedding Methods}} and {{Their Applications}}},
  author = {Xu, Mengjia},
  date = {2021-01},
  journaltitle = {SIAM Review},
  shortjournal = {SIAM Rev.},
  volume = {63},
  number = {4},
  pages = {825--853},
  issn = {0036-1445, 1095-7200},
  doi = {10.1137/20M1386062},
  url = {https://epubs.siam.org/doi/10.1137/20M1386062},
  urldate = {2025-09-19},
  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.},
  langid = {english},
  keywords = {/unread},
  annotation = {Read\_Status: New\\
Read\_Status\_Date: 2025-09-19T12:36:55.370Z},
  file = {/home/louis/snap/zotero-snap/common/Zotero/storage/WLYYXUGK/Xu - 2021 - Understanding Graph Embedding Methods and Their Applications.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/louis/snap/zotero-snap/common/Zotero/storage/DIXT2PYL/insect-pollinators-of-the-mer-bleue-peat-bog-of-ottawa-biodiversity-.html}
}

@article{zhouPredictingMissingLinks2009,
  title = {Predicting Missing Links via Local Information},
  author = {Zhou, Tao and Lü, Linyuan and Zhang, Yi-Cheng},
  date = {2009-10},
  journaltitle = {The European Physical Journal B},
  shortjournal = {Eur. Phys. J. B},
  volume = {71},
  number = {4},
  pages = {623--630},
  issn = {1434-6028, 1434-6036},
  doi = {10.1140/epjb/e2009-00335-8},
  url = {http://link.springer.com/10.1140/epjb/e2009-00335-8},
  urldate = {2025-04-11},
  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.},
  langid = {english},
  keywords = {/unread},
  file = {/home/louis/snap/zotero-snap/common/Zotero/storage/XMRKAJWG/Zhou et al. - 2009 - Predicting missing links via local information.pdf}
}

@article{zhouPredictingMissingLinks2009a,
  title = {Predicting Missing Links via Local Information},
  author = {Zhou, Tao and Lü, Linyuan and Zhang, Yi-Cheng},
  date = {2009-10-01},
  journaltitle = {The European Physical Journal B},
  shortjournal = {Eur. Phys. J. B},
  volume = {71},
  number = {4},
  pages = {623--630},
  issn = {1434-6036},
  doi = {10.1140/epjb/e2009-00335-8},
  url = {https://doi.org/10.1140/epjb/e2009-00335-8},
  urldate = {2025-04-11},
  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.},
  langid = {english},
  keywords = {/unread,05.65.+b Self-organized systems,89.75.-k Complex systems},
  file = {/home/louis/snap/zotero-snap/common/Zotero/storage/7XXZZDDY/Zhou et al. - 2009 - Predicting missing links via local information.pdf}
}