Block modelling in dynamic networks with non-homogeneous Poisson processes and exact ICL

Abstract : We develop a model in which interactions between nodes of a dynamic network are counted by non homogeneous Poisson processes. In a block modelling perspective, nodes belong to hidden clusters (whose number is unknown) and the intensity functions of the counting processes only depend on the clusters of nodes. In order to make inference tractable we move to discrete time by partitioning the entire time horizon in which interactions are observed in fixed-length time sub-intervals. First, we derive an exact integrated classification likelihood criterion and maximize it relying on a greedy search approach. This allows to estimate the memberships to clusters and the number of clusters simultaneously. Then a maximum-likelihood estimator is developed to estimate non parametrically the integrated intensities. We discuss the over-fitting problems of the model and propose a regularized version solving these issues. Experiments on real and simulated data are carried out in order to assess the proposed methodology.
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Social Network Analysis and Mining, Springer, 2016, 6, <10.1007/s13278-016-0368-3>
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https://hal.archives-ouvertes.fr/hal-01468548
Contributeur : Fabrice Rossi <>
Soumis le : lundi 10 juillet 2017 - 09:55:46
Dernière modification le : mardi 11 juillet 2017 - 01:10:54

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Marco Corneli, Pierre Latouche, Fabrice Rossi. Block modelling in dynamic networks with non-homogeneous Poisson processes and exact ICL. Social Network Analysis and Mining, Springer, 2016, 6, <10.1007/s13278-016-0368-3>. <hal-01468548v2>

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