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

Marco Corneli (1) , Pierre Latouche (1) , Fabrice Rossi (1)

1 SAMM - Statistique, Analyse et Modélisation Multidisciplinaire (SAmos-Marin Mersenne)

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.

Mots clés: Graph clustering; Temporal graph; Stochastic Block Model; Latent Block Model; Integrated Complete-data Likelihood

Domaines: Machine Learning [stat.ML]

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. ⟨hal-01163367⟩