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Uncovering latent structure in valued graphs: a variational approach

Abstract : 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.
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Mahendra Mariadassou, Stephane Robin, Corinne Vacher. Uncovering latent structure in valued graphs: a variational approach. Annals of Applied Statistics, 2010, 4 (2), pp.715-742. ⟨10.1214/10-AOAS361⟩. ⟨hal-01197514⟩



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