A unified generative Bayesian model for community discovery and role assignment based upon latent interaction factors

We propose a generative probabilistic approach to modeling interactions in directed graphs for unveiling the participation of nodes in multiple communities along with the roles played therein. Precisely, a hierarchical Bayesian model is developed, in which node interactions are governed by latent explicative reasons regarded as personal and contextual interaction factors. The former are inherently descriptive of nodes, while the latter are characterizations of individual communities and roles. The generative process of the devised model assigns nodes to communities with respective roles and connects them through directed links, that are ruled by their personal and contextual interaction factors.We derive posterior inference for the presented model based on approximate Markov-Chain Monte-Carlo methods. Finally, we demonstrate via a comparative analysis on real-world networks the superior performance of our approach in terms of community compactness and predictive power of the discovered communities, roles and interaction factors.