Estimation et clustering dans un modèle à blocs stochastiques pour des réseaux d’interaction longitudinaux

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Date(s) - 08/04/2019
14 h 00 min - 15 h 00 min

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In this work, we introduce a Poisson process stochastic block model for recurrent interaction events, where each individual belongs to a latent group and interactions between two individuals follow a conditional inhomogeneous Poisson process whose intensity is driven by the individuals’ latent groups. The model is semiparametric as the
intensities per group pair are modeled in a nonparametric way.
We propose an estimation procedure, relying on a semiparametric version of a variational expectation-maximization algorithm. Two different versions of the method are proposed, using either histogram-type (with an adaptive choice of the partition size) or kernel intensity estimators. We also propose an integrated classification likelihood criterion to select the number of latent groups. We carry out synthetic experiments and analyse different real datasets to illustrate our approach.
This is joint work with Catherine Matias and Tabea Rebafka.

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