An iterated local search algorithm for community detection in signed networks

Community detection in signed networks has become a research hotspot in the area of network science recently. Since the classical problem has great significance for theoretical analysis and practical application, many heuristics or metaheuristics have been presented. Despite some progress and results that have been achieved, it remains an open challenge to identify community structure in large signed networks. In this paper, we propose a simple and effective iterated local search algorithm coupled with a powerful local search mechanism to solve the community detection problem. Due to the limitation of modularity in resolution, the modularity density criterion is adopted to find communities in signed networks. Extensive experiments have been conducted on synthetic and real-world networks. The statistical analyses demonstrate that the proposed algorithm can provide high-quality solutions compared to the state-of-the-art algorithms.