Uncovering latent structure in valued graphs: A variational approach
Mahendra Mariadassou, Stéphane Robin, Corinne Vacher
The Annals of Applied Statistics · 2010 · Volume 4 · Issue 2
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.
Keywords
Ecological networks, host–parasite interactions, latent structure, mixture model, random graph, valued graph, variational method
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