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The latent block model (LBM) is a flexible probabilistic tool to describeinteractions between node sets in bipartite networks, but it does not accountfor interactions of time varying intensity between nodes in unknown classes. Inthis paper we propose a non stationary temporal extension of the LBM thatclusters simultaneously the two node sets of a bipartite network and constructsclasses of time intervals on which interactions are stationary. The number ofclusters as well as the membership to classes are obtained by maximizing theexact complete-data integrated likelihood relying on a greedy search approach.Experiments on simulated and real data are carried out in order to assess theproposed methodology.

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