A node-priority based large-scale overlapping community detection using evolutionary multi-objective optimization

Community structure is one of the most important features in complex networks. However, with increasing of network scale, some existing methods cannot effectively detect the community structure of complex network, and the available methods mostly aimed at non-overlapping networks. In this paper, we focus on overlapping community detection in large-scale networks, because most of the communities in real-world networks are overlapped. In order to improve the accuracy of large-scale overlapping community detection, we suggest a community detection method based on node priority. The proposed algorithm has two advantages: (1) We define a priority function fNN to assess the closeness between adjacent nodes. It explores the potential community structure in advance and reduces the scale of networks. (2) We employ NSGA-II and select all Pareto fronts to mine large-scale overlapping communities. The proposed algorithm is tested by the artificial and real datasets. The results show that the proposed algorithm can effectively improve the accuracy of community detection and has better optimization effect.