52 lines
5.7 KiB
BibTeX
52 lines
5.7 KiB
BibTeX
@misc{anakokDisentanglingStructureEcological2022,
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title = {Disentangling the Structure of Ecological Bipartite Networks from Observation Processes},
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author = {Anakok, Emre and Barbillon, Pierre and Fontaine, Colin and Thebault, Elisa},
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year = {2022},
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month = nov,
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number = {arXiv:2211.16364},
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eprint = {2211.16364},
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primaryclass = {stat},
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publisher = {{arXiv}},
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urldate = {2023-06-14},
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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.},
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archiveprefix = {arxiv},
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langid = {english},
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keywords = {Statistics - Methodology},
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file = {/home/polarolouis/Zotero/storage/LQ3FINZG/Anakok et al. - 2022 - Disentangling the structure of ecological bipartit.pdf}
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}
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@article{celisseConsistencyMaximumlikelihoodVariational2012,
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title = {Consistency of Maximum-Likelihood and Variational Estimators in the Stochastic Block Model},
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author = {Celisse, Alain and Daudin, Jean-Jacques and Pierre, Laurent},
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year = {2012},
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month = jan,
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journal = {Electronic Journal of Statistics},
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volume = {6},
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number = {none},
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pages = {1847--1899},
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publisher = {{Institute of Mathematical Statistics and Bernoulli Society}},
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issn = {1935-7524, 1935-7524},
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doi = {10.1214/12-EJS729},
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urldate = {2023-06-06},
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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.},
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keywords = {62E17,62G05,62G20,62H30,Concentration inequalities,consistency,maximum likelihood estimators,Random graphs,Stochastic block model,variational estimators},
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file = {/home/polarolouis/Zotero/storage/JNWRIYKG/celisse2012.pdf.pdf;/home/polarolouis/Zotero/storage/XG463B5I/Celisse et al. - 2012 - Consistency of maximum-likelihood and variational .pdf}
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}
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@misc{chabert-liddellLearningCommonStructures2023,
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type = {Article},
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title = {Learning Common Structures in a Collection of Networks. {{An}} Application to Food Webs},
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author = {{Chabert-Liddell}, Saint-Clair and Barbillon, Pierre and Donnet, Sophie},
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year = {2023},
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month = mar,
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number = {arXiv:2206.00560},
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eprint = {2206.00560},
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primaryclass = {stat},
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publisher = {{arXiv}},
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doi = {10.48550/arXiv.2206.00560},
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urldate = {2023-05-22},
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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.},
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archiveprefix = {arxiv},
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keywords = {Statistics - Applications,Statistics - Methodology},
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file = {/home/polarolouis/Zotero/storage/M74TXGCF/Chabert-Liddell et al. - 2023 - Learning common structures in a collection of netw.pdf;/home/polarolouis/Zotero/storage/A35M8KNP/2206.html}
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}
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