Open Access
2014 Model selection in overlapping stochastic block models
Pierre Latouche, Etienne Birmelé, Christophe Ambroise
Electron. J. Statist. 8(1): 762-794 (2014). DOI: 10.1214/14-EJS903


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.


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Pierre Latouche. Etienne Birmelé. Christophe Ambroise. "Model selection in overlapping stochastic block models." Electron. J. Statist. 8 (1) 762 - 794, 2014.


Published: 2014
First available in Project Euclid: 9 June 2014

zbMATH: 1349.62276
MathSciNet: MR3217788
Digital Object Identifier: 10.1214/14-EJS903

Keywords: global and local variational techniques , Graph clustering , Model selection , overlapping stochastic block models , Random graph models

Rights: Copyright © 2014 The Institute of Mathematical Statistics and the Bernoulli Society

Vol.8 • No. 1 • 2014
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