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2 octobre 2017

Indéfini
Heure et lieu: 
11h, salle de réunion, bâtiment 210
Nom intervenant: 
Pierre Latouche
Titre: 
Multiple change points detection and clustering in dynamic networks
Résumé: 

The increasing amount of data stored in 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 work, 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 change points detection in 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.

Année: 
2017
Organisme intervenant: 
Université Paris 1, Laboratoire SAMM
Date du jour: 
Lundi, Octobre 2, 2017


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