Statistical clustering of temporal networks through a dynamic stochastic block model: Clustering dynamic random graphs via SBM

Abstract : Statistical node clustering in discrete time dynamic networks is an emerging field that raises many challenges. Here, we explore statistical properties and frequentist inference in a model that combines a stochastic block model (SBM) for its static part with independent Markov chains for the evolution of the nodes groups through time. We model binary data as well as weighted dynamic random graphs (with discrete or continuous edges values). Our approach, motivated by the importance of controlling for label switching issues across the different time steps, focuses on detecting groups characterized by a stable within group connectivity behavior. We study identifiability of the model parameters , propose an inference procedure based on a variational expectation maximization algorithm as well as a model selection criterion to select for the number of groups. We carefully discuss our initialization strategy which plays an important role in the method and compare our procedure with existing ones on synthetic datasets. We also illustrate our approach on dynamic contact networks, one of encounters among high school students and two others on animal interactions. An implementation of the method is available as a R package called dynsbm.
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Journal of the Royal Statistical Society: Series B, Royal Statistical Society, 2016, <10.1111/rssb.12200>
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https://hal.archives-ouvertes.fr/hal-01167837
Contributeur : Catherine Matias <>
Soumis le : vendredi 5 février 2016 - 13:56:39
Dernière modification le : lundi 29 mai 2017 - 14:26:15

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Catherine Matias, Vincent Miele. Statistical clustering of temporal networks through a dynamic stochastic block model: Clustering dynamic random graphs via SBM. Journal of the Royal Statistical Society: Series B, Royal Statistical Society, 2016, <10.1111/rssb.12200>. <hal-01167837v2>

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