Detecting overlapping communities in networks via dominant label propagation**Project supported by the National Natural Science Foundation of China (Grant Nos. 61173093 and 61202182), the Postdoctoral Science Foundation of China(Grant No. 2012 M521776), the Fundamental Research Funds for the Central Universities of China, the Postdoctoral Science Foundation of Shannxi Province,China, and the Natural Science Basic Research Plan of Shaanxi Province, China

Abstract

Community detection is an important methodology for understanding the intrinsic structure and function of a real-world network. In this paper, we propose an effective and efficient algorithm, called Dominant Label Propagation Algorithm (Abbreviated as DLPA), to detect communities in complex networks. The algorithm simulates a special voting process to detect overlapping and non-overlapping community structure in complex networks simultaneously. Our algorithm is very efficient, since its computational complexity is almost linear to the number of edges in the network. Experimental results on both real-world and synthetic networks show that our algorithm also possesses high accuracies on detecting community structure in networks.