  We propose a framework to model the distribution of sequential data coming
from a set of entities connected in a graph with a known topology. The method
is based on a mixture of shared hidden Markov models (HMMs), which are jointly
trained in order to exploit the knowledge of the graph structure and in such a
way that the obtained mixtures tend to be sparse. Experiments in different
application domains demonstrate the effectiveness and versatility of the
method.
