Overlapping Communities and Roles in Networks with Node Attributes: Probabilistic Graphical Modeling, Bayesian Formulation and Variational Inference (Extended Abstract)

We study the seamless integration of community discovery and behavioral role analysis, in the domain of networks with node attributes. In particular, we focus on unifying the two tasks, by explicitly harnessing node attributes and behavioral role patterns in a principled manner. To this end, we propose two Bayesian probabilistic generative models of networks, whose novelty consists in the interrelationship of overlapping communities, roles, their behavioral patterns and node attributes. The devised models allow for a variety of exploratory, descriptive and predictive tasks. These are carried out through mean-field variational inference, which is in turn mathematically derived and implemented into a coordinate-ascent algorithm. A wide spectrum of experiments is designed, to validate the devised models against three classes of state-of-the-art competitors using various real-world benchmark data sets from different social networking services.