A semiparametric extension of the stochastic block model for longitudinal networks: Semiparametric estimation in PPSBMReport as inadecuate




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1 LPMA - Laboratoire de Probabilités et Modèles Aléatoires

Abstract : To model recurrent interaction events in continuous time, an extension of the stochastic block model is proposed where every individual belongs to a latent group and interactions between two individuals follow a conditional inhomogeneous Poisson process with intensity driven by the individuals- latent groups. The model is shown to be identifiable and its estimation is based on a semiparametric variational expectation-maximization algorithm. Two versions of the method are developed, using either a nonparametric histogram approach with an adaptive choice of the partition size or kernel intensity estimators. The number of latent groups can be selected by an integrated classification likelihood criterion. Finally, we demonstrate the performance of our procedure on synthetic experiments, analyse two datasets to illustrate the utility of our approach and comment on competing methods.

Keywords : link streams integrated classification likelihood expectation-maximization algorithm dynamic interactions longitudinal network semiparametric model variational approximation Temporal network





Author: Catherine Matias - Tabea Rebafka - Fanny Villers -

Source: https://hal.archives-ouvertes.fr/



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