文章基本信息

标题：Model selection in overlapping stochastic block models
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作者：Pierre Latouche ; Etienne Birmelé ; Christophe Ambroise 等
期刊名称：Electronic Journal of Statistics
印刷版ISSN：1935-7524
出版年度：2014
卷号：8
期号：1
页码：762-794
DOI：10.1214/14-EJS903
语种：English
出版社：Institute of Mathematical Statistics
摘要：Networks are a commonly used mathematical model to describe the rich set of interactions between objects of interest. Many clustering methods have been developed in order to partition such structures, among which several rely on underlying probabilistic models, typically mixture models. The relevant hidden structure may however show overlapping groups in several applications. The Overlapping Stochastic Block Model (Latouche, Birmelé and Ambroise (2011)) has been developed to take this phenomenon into account. Nevertheless, the problem of the choice of the number of classes in the inference step is still open. To tackle this issue, we consider the proposed model in a Bayesian framework and develop a new criterion based on a non asymptotic approximation of the marginal log-likelihood. We describe how the criterion can be computed through a variational Bayes EM algorithm, and demonstrate its efficiency by running it on both simulated and real data.
关键词：Graph clustering;random graph models;over lapping stochastic block models;model selection;global and local varia tional techniques.