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hal.structure.identifierUnité Mathématique Informatique et Génome [MIG]
dc.contributor.authorMARIADASSOU, Mahendra
hal.structure.identifierMathématiques et Informatique Appliquées [MIA-Paris]
dc.contributor.authorROBIN, Stephane
hal.structure.identifierBiodiversité, Gènes & Communautés [BioGeCo]
dc.contributor.authorVACHER, Corinne
dc.date.issued2010
dc.identifier.issn1932-6157
dc.description.abstractEnAs more and more network-structured data sets are available, the statistical analysis of valued graphs has become common place. Looking for a latent structure is one of the many strategies used to better understand the behavior of a network. Several methods already exist for the binary case. We present a model-based strategy to uncover groups of nodes in valued graphs. This framework can be used for a wide span of parametric random graphs models and allows to include covariates. Variational tools allow us to achieve approximate maximum likelihood estimation of the parameters of these models. We provide a simulation study showing that our estimation method performs well over a broad range of situations. We apply this method to analyze host parasite interaction networks in forest ecosystems.
dc.language.isoen
dc.publisherInstitute of Mathematical Statistics
dc.subject.enECOLOGICAL NETWORKS
dc.subject.enHOST–PARASITE INTERACTIONS
dc.subject.enLATENT STRUCTURE
dc.subject.enMIXTURE
dc.subject.enMODEL
dc.subject.enRANDOM GRAPH
dc.subject.enVALUED GRAPH
dc.subject.enVARIATIONAL METHOD
dc.subject.enRELATION HOTE-PARASITE
dc.title.enUncovering latent structure in valued graphs: a variational approach
dc.typeArticle de revue
dc.identifier.doi10.1214/10-AOAS361
dc.subject.halSciences du Vivant [q-bio]
bordeaux.journalAnnals of Applied Statistics
bordeaux.page715-742
bordeaux.volume4
bordeaux.issue2
bordeaux.peerReviewedoui
hal.identifierhal-01197514
hal.version1
hal.popularnon
hal.audienceNon spécifiée
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-01197514v1
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