  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.
