  A natural approach to analyze interaction data of form
"what-connects-to-what-when" is to create a time-series (or rather a sequence)
of graphs through temporal discretization (bandwidth selection) and spatial
discretization (vertex contraction). Such discretization together with
non-negative factorization techniques can be useful for obtaining clustering of
graphs. Motivating application of performing clustering of graphs (as opposed
to vertex clustering) can be found in neuroscience and in social network
analysis, and it can also be used to enhance community detection (i.e., vertex
clustering) by way of conditioning on the cluster labels. In this paper, we
formulate a problem of clustering of graphs as a model selection problem. Our
approach involves information criteria, non-negative matrix factorization and
singular value thresholding, and we illustrate our techniques using real and
simulated data.
