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June 2010 Uncovering latent structure in valued graphs: A variational approach
Mahendra Mariadassou, Stéphane Robin, Corinne Vacher
Ann. Appl. Stat. 4(2): 715-742 (June 2010). DOI: 10.1214/10-AOAS361

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. Stéphane Robin. Corinne Vacher. "Uncovering latent structure in valued graphs: A variational approach." Ann. Appl. Stat. 4 (2) 715 - 742, June 2010. https://doi.org/10.1214/10-AOAS361

Information

Published: June 2010
First available in Project Euclid: 3 August 2010

zbMATH: 1194.62125
MathSciNet: MR2758646
Digital Object Identifier: 10.1214/10-AOAS361

Rights: Copyright © 2010 Institute of Mathematical Statistics

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Vol.4 • No. 2 • June 2010
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