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

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.
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Pré-publication, Document de travail
2017
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https://hal.archives-ouvertes.fr/hal-01245867
Contributeur : Catherine Matias <>
Soumis le : vendredi 21 juillet 2017 - 17:58:44
Dernière modification le : jeudi 27 juillet 2017 - 01:11:39

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dynppsbm_arxiv_V3.pdf
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  • HAL Id : hal-01245867, version 3
  • ARXIV : 1512.07075

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PMA | UPMC | USPC

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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. 2017. <hal-01245867v3>

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