Uncovering latent structure in valued graphs: A variational approach

Details Uncovering latent structure in valued graphs: A variational approach Uncovering latent structure in valued graphs: A variational approach

2010-11-08

arxiv.org/abs/1011.1813v1

Annals of Applied Statistics 2010, Vol. 4, No. 2, 715-742

Statistics

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Published in at http://dx.doi.org/10.1214/10-AOAS361 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Ins

Scientific paper

10.1214/10-AOAS361

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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