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 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.
A method to model the generative distribution of sequences coming from graph connected entities. The authors propose a method to model sequential data from multiple interconnected sources using a mixture of common pool of HMM's.