Exact ICL maximization in a non-stationary time extension of the latent block model for dynamic networks
This work addresses the challenge of analyzing dynamic bipartite networks for researchers in network analysis, though it appears incremental as it extends an existing model.
The authors tackled the problem of modeling dynamic bipartite networks with time-varying interaction intensities by proposing a non-stationary temporal extension of the latent block model, which simultaneously clusters node sets and time intervals, and they validated it through experiments on simulated and real data.
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.