A Conical Area Evolutionary Algorithm Based on Modularity Q for Community Detection from Signed Networks

Most of the existing community detection (CD) methods are designed primarily for unsigned networks containing only positive links. Therefore, it is significant to explore and design effective CD methods for signed social networks (SNs) with both positive and negative links. In this paper, we first utilize decomposable characteristic of modularity Q to establish a bi-objective model for community detection from SNs. Afterwards, a conical area evolutionary algorithm based on the modularity Q (CAEAq-SN) is developed to solve this bi-objective model efficiently. Furthermore, a new tournament selection mechanism based on Q is applied to accelerate the convergence of Q. Experimental results on both benchmark networks and synthetic SNs indicate that CAEAq-SN achieves not only better community structures in term of both Q and NMI but also stronger robustness than the existing algorithm MEAs-SN.