A semiparametric extension of the stochastic block model for longitudinal networks: Semiparametric estimation in PPSBM

Abstract : To model recurrent interaction events in continuous time, we propose an extension of the stochastic block model 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 shown to be identifiable and an estimation procedure is proposed 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 and the analysis of several real datasets illustrates the utility of our approach.
Type de document :
Pré-publication, Document de travail
2016
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https://hal.archives-ouvertes.fr/hal-01245867
Contributeur : Catherine Matias <>
Soumis le : lundi 11 juillet 2016 - 09:37:09
Dernière modification le : jeudi 27 avril 2017 - 09:45:50

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dynppsbm_revised.pdf
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  • HAL Id : hal-01245867, version 2
  • ARXIV : 1512.07075

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UPMC | USPC | PMA

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Catherine Matias, Tabea Rebafka, Fanny Villers. A semiparametric extension of the stochastic block model for longitudinal networks: Semiparametric estimation in PPSBM. 2016. <hal-01245867v2>

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