Exact ICL maximization in a non-stationary temporal extension of the stochastic block model for dynamic networks
This work addresses the limitation of the stochastic block model in handling non-stationary temporal dynamics in networks, which is an incremental improvement for researchers in network analysis and dynamic modeling.
The paper tackles the problem of modeling dynamic networks with time-varying interaction intensities by extending the stochastic block model to include a temporal partition, enabling simultaneous clustering of nodes and time intervals. The result is an inference procedure that maximizes an exact integrated complete-data likelihood using a greedy search approach, validated on simulated and real data.
The stochastic block model (SBM) is a flexible probabilistic tool that can be used to model interactions between clusters of nodes in a network. However, it does not account for interactions of time varying intensity between clusters. The extension of the SBM developed in this paper addresses this shortcoming through a temporal partition: assuming interactions between nodes are recorded on fixed-length time intervals, the inference procedure associated with the model we propose allows to cluster simultaneously the nodes of the network and the time intervals. The number of clusters of nodes and of time intervals, as well as the memberships to clusters, are obtained by maximizing an exact integrated complete-data likelihood, relying on a greedy search approach. Experiments on simulated and real data are carried out in order to assess the proposed methodology.