Mining Cohesive Clusters with Interpretations in Labeled Graphs

Hongxia Du, Heli Sun, Jianbin Huang, Zhongbin Sun, Liang He, Hong Cheng

Abstract: In recent years, community detection on plain graphs has been widely studied. With the proliferation of available data, each user in the network is usually associated with additional attributes for elaborate description. However, many existing methods only focus on the topological structure and fail to deal with node-attributed networks. These approaches cannot extract clear semantic meanings for communities detected. In this paper, we combine the topological structure and attribute information into a unified process and propose a novel algorithm to detect overlapping semantic communities. The proposed algorithm is divided into three phases. Firstly, we detect local semantic subcommunities from each node’s perspective using a greedy strategy. Then, a supergraph which consists of all these subcommunities is created. Finally, we find global semantic communities on the supergraph. The experimental results on real-world datasets show the efficiency and effectiveness of our approach against other state-of-the-art methods.