Manifold regularized symmetric joint link model for overlapping community detection

Abstract

Overlapping community detection is an important research topic in analyzing real-world networks. Among existing algorithms for detecting overlapping communities, generative models have shown their superiorities. However, previous generative models do not consider the intrinsic geometry of probability distribution manifold. To tackle this problem, we propose a Manifold Regularized Symmetric Joint Link Model (MSJL), which utilizes the local geometrical structure of manifold to improve the performance of overlapping community detection. MSJL assumes that the community probability distribution lives on a submanifold, and adopts the manifold assumption which specifically requires two close nodes in an intrinsic geometry to have similar community distribution. The structure of the intrinsic manifold is modeled by a nearest neighbor graph, and MSJL incorporates the graph Laplacian as a manifold regularization into the maximum likelihood function of the standard SJL model. Experiments on synthetic benchmarks and real-world networks demonstrate that MSJL can significantly improve the performance compared with the state-of-the-art methods.