Exact ICL maximization in a non-stationary time extension of the latent block model for dynamic networks

Abstract : The latent block model (LBM) is a flexible probabilistic tool to describe interactions between node sets in bipartite networks, but it does not account for interactions of time varying intensity between nodes in unknown classes. In this paper we propose a non stationary temporal extension of the LBM that clusters simultaneously the two node sets of a bipartite network and constructs classes of time intervals on which interactions are stationary. The number of clusters as well as the membership to classes are obtained by maximizing the exact complete-data integrated likelihood relying on a greedy search approach. Experiments on simulated and real data are carried out in order to assess the proposed methodology.
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Communication dans un congrès
European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), Apr 2015, Bruges, Belgium. pp.225-230, 2015, Proceedings of the 23-th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2015)
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Contributeur : Fabrice Rossi <>
Soumis le : vendredi 12 juin 2015 - 16:30:32
Dernière modification le : samedi 13 juin 2015 - 01:05:27

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  • HAL Id : hal-01163367, version 1
  • ARXIV : 1506.04138

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Marco Corneli, Pierre Latouche, Fabrice Rossi. Exact ICL maximization in a non-stationary time extension of the latent block model for dynamic networks. European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), Apr 2015, Bruges, Belgium. pp.225-230, 2015, Proceedings of the 23-th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2015). <hal-01163367>

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