Local Optimization for Clique-Based Overlapping Community Detection in Complex Networks

Abstract

Detecting communities in complex networks has been one of the most popular research areas in recent years. There have been many community detection algorithms proposed to date. However, the local information (cliques) of communities and the search efficiency of algorithm have not been considered both in previous studies. In this paper, we propose a novel local expansion algorithm for detecting overlapping communities based on cliques. The algorithm draws on the assumption that cliques are the core of communities, as the clique takes into account the local characteristics of the community. The proposed algorithm adopts a single node with the maximum density as an initial community to prevent the formation of a large number of near-duplicate community structures, which improves the search efficiency of the algorithm. In many experiments using computer-generated and real-world networks, the proposed algorithm based on this idea verifies that the algorithm is able to detect overlapping communities effectively. The experiment yields better community uncover results, and the time efficiency and the complexity of algorithm are also satisfactory.