Multiple change points detection and clustering in dynamic network

Abstract : The increasing amount of data stored in the form of dynamic interactions between actors necessitates the use of methodologies to automatically extract relevant information. The interactions can be represented by dynamic networks in which most existing methods look for clusters of vertices to summarize the data. In this paper, a new framework is proposed in order to cluster the vertices while detecting change points in the intensities of the interactions. These change points are key in the understanding of the temporal interactions. The model used involves non homogeneous Poisson point processes with cluster dependent piecewise constant intensity functions and common discontinuity points. A variational expectation maximization algorithm is derived for inference. We show that the pruned exact linear time method, originally developed for univariate time series, can be considered for the maximization step. This allows the detection of both the number of change points and their location. Experiments on artificial and real datasets are carried out and the proposed approach is compared with related methods. Keywords Dynamic networks · non homogeneous Poisson point processes · stochastic block model · variational EM · PELT M. Corneli
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Soumis le : mardi 10 janvier 2017 - 10:53:05
Dernière modification le : samedi 14 janvier 2017 - 01:10:16
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  • HAL Id : hal-01430717, version 1



Marco Corneli, Pierre Latouche, Fabrice Rossi. Multiple change points detection and clustering in dynamic network. 2017. 〈hal-01430717〉



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