Identification of Generalized Semantic Communities in Large Social Networks

Community detection in networks is a fundamental data analysis task. Recently, researchers have tried to improve its performance by exploiting semantic contents and interpret the communities. However, they typically assume that communities are assortative (i.e. vertices are mostly connected to others within the group), thus they cannot find the generalized community structures, which includes assortative communities, disassortative communities (i.e. most connections are from two groups), or a combination. In addition, they often assume that each group membership corresponds to a single topic, thus they cannot perform well when the contents are not consistent with community structures. To address these two issues, we propose a new Bayesian model and develop an efficient variational inference algorithm for model inference. This model describes the generalized communities and the topical clusters separately, and explores their latent correlation simultaneously to make the two parts mutually reinforcing. Our model is not only robust to the above problems, but also can interpret each community using more than one topic. We validate the robustness of this approach on an artificial benchmark, and analyze its interpretability by a case study. We finally show its superior community detection performance by comparing with eight state-of-the-art algorithms on eight real networks.