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• W2018312367 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." @default.
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• W2018312367 date "2010-06-01" @default.
• W2018312367 modified "2023-10-14" @default.
• W2018312367 title "Uncovering latent structure in valued graphs: A variational approach" @default.
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• W2018312367 doi "https://doi.org/10.1214/10-aoas361" @default.
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