MARIADASSOU, Mahendra; ROBIN, Stephane; VACHER, Corinne

doi:10.1214/10-AOAS361

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MARIADASSOU, Mahendra
Unité Mathématique Informatique et Génome [MIG]

ROBIN, Stephane
Mathématiques et Informatique Appliquées [MIA-Paris]

VACHER, Corinne
Biodiversité, Gènes & Communautés [BioGeCo]

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Annals of Applied Statistics. 2010, vol. 4, n° 2, p. 715-742

Institute of Mathematical Statistics

Résumé en anglais

As 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.< Réduire

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ECOLOGICAL NETWORKS

HOST–PARASITE INTERACTIONS

LATENT STRUCTURE

MIXTURE

MODEL

RANDOM GRAPH

VALUED GRAPH

VARIATIONAL METHOD

RELATION HOTE-PARASITE

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Uncovering latent structure in valued graphs: a variational approach

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