  In this paper, we focus on the stochastic block model (SBM),a probabilistic
tool describing interactions between nodes of a network using latent clusters.
The SBM assumes that the networkhas a stationary structure, in which
connections of time varying intensity are not taken into account. In other
words, interactions between two groups are forced to have the same features
during the whole observation time. To overcome this limitation,we propose a
partition of the whole time horizon, in which interactions are observed, and
develop a non stationary extension of the SBM,allowing to simultaneously
cluster the nodes in a network along with fixed time intervals in which the
interactions take place. The number of clusters (K for nodes, D for time
intervals) as well as the class memberships are finallyobtained through
maximizing the complete-data integrated likelihood by means of a greedy search
approach. After showing that the model works properly with simulated data, we
focus on a real data set. We thus consider the three days ACM Hypertext
conference held in Turin,June 29th - July 1st 2009. Proximity interactions
between attendees during the first day are modelled and an
interestingclustering of the daily hours is finally obtained, with times of
social gathering (e.g. coffee breaks) recovered by the approach. Applications
to large networks are limited due to the computational complexity of the greedy
search which is dominated bythe number $K\_{max}$ and $D\_{max}$ of clusters
used in the initialization. Therefore,advanced clustering tools are considered
to reduce the number of clusters expected in the data, making the greedy search
applicable to large networks.
