  This paper introduces a novel technique to track structures in time varying
graphs. The method uses a maximum a posteriori approach for adjusting a
three-dimensional co-clustering of the source vertices, the destination
vertices and the time, to the data under study, in a way that does not require
any hyper-parameter tuning. The three dimensions are simultaneously segmented
in order to build clusters of source vertices, destination vertices and time
segments where the edge distributions across clusters of vertices follow the
same evolution over the time segments. The main novelty of this approach lies
in that the time segments are directly inferred from the evolution of the edge
distribution between the vertices, thus not requiring the user to make any a
priori quantization. Experiments conducted on artificial data illustrate the
good behavior of the technique, and a study of a real-life data set shows the
potential of the proposed approach for exploratory data analysis.
