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.
Keywords
Cite
@article{arxiv.1011.1813,
title = {Uncovering latent structure in valued graphs: A variational approach},
author = {Mahendra Mariadassou and Stéphane Robin and Corinne Vacher},
journal= {arXiv preprint arXiv:1011.1813},
year = {2010}
}
Comments
Published in at http://dx.doi.org/10.1214/10-AOAS361 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)