Modeling group dynamics using graphical models and tensor decompositions

We propose a general modeling framework for learning group dynamics in data collected from multiple information sources and over time. In particular, groups are characterized by specific temporal patterns based on hidden Markov models. Tensor decomposition techniques combined with graphical models are used to extract group information from the observed data.