A parallel multi-objective evolutionary algorithm for community detection in large-scale complex networks

Community detection in large-scale complex networks has recently received significant attention as the volume of available data is becoming larger. The use of evolutionary algorithms (EAs) for community detection in large-scale networks has gained considerable popularity because these algorithms are fairly effective in networks with a relatively small number of nodes. In this paper, we propose a parallel multi-objective EA, called PMOEA, for community detection in large-scale networks, where the communities associated with key network nodes are detected in parallel. Specifically, we develop a multi-objective and a single-objective EA. The former is used to detect the communities of a key node instead of all communities in the network. The latter obtains the communities in the entire network using the previously detected communities of each key node. The performance of the proposed method was verified on both large-scale synthetic benchmark networks and real-world networks. The results demonstrated the superiority of PMOEA over six EA-based and two non-EA-based community-detection algorithms for large-scale networks.