Uncovering Latent Structure in Valued Graphs: A Variational Approach
vendredi 11 juin 2010, 9h30 - 10h45
As more and more network-structured datasets 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 analyse interaction networks of tree and