Towards Efficient Detection of Overlapping Communities in Massive Networks

Abstract

Community detection is essential to analyzing and exploring natural networks such as social networks, biological networks, and citation networks. However, few methods could be used as off-the-shelf tools to detect communities in real world networks for two reasons. On the one hand, most existing methods for community detection cannot handle massive networks that contain millions or even hundreds of millions of nodes. On the other hand, communities in real world networks are generally highly overlapped, requiring that community detection method could capture the mixed community membership. In this paper, we aim to offer an off-the-shelf method to detect overlapping communities in massive real world networks. For this purpose, we take the widely-used Poisson model for overlapping community detection as starting point and design two speedup strategies to achieve high efficiency. Extensive tests on synthetic and large scale real networks demonstrate that the proposed strategies speedup the community detection method based on Poisson model by 1 to 2 orders of magnitudes, while achieving comparable accuracy at community detection.