A Bayesian Hierarchical Approach for Exploratory Analysis of Communities and Roles in Social Networks

Abstract

We present a new probabilistic approach to modeling social interactions, that seamlessly integrates community discovery and role assignment for a deeper understanding of connectivity patterns in social networks. The devised approach is an unsupervised learning technique based on a Bayesian hierarchical model of social interactions. This model specifies an intuitive generative process, in which pairs of nodes in a social network are associated with communities as well as roles in the context of the respective communities, before that a directed interaction is possibly established between them. According to the generative semantics of the proposed model, nodes are represented as probability distributions over communities, while communities are represented as probability distributions over roles. Such distributions are unknown parameters of the proposed model, that are estimated from social-network data through approximated posterior inference and parameter estimation. A comparative evaluation over real-world social networks reveals that our approach outperforms state-of-the-art competitors in terms of link prediction.