Abstract
Community detection is one of the most challenging problems in complex network analysis. This problem attracts an amount of interest from various scientific fields such as biology, social network and physics. In the past few decades, numerous heuristics and exact algorithms have been designed to address the problem. However, most of them are not suitable for large networks, since they require considerable computing time. Contrary to the recent trend in the community detection literature, where complex nature-inspired methods are often proposed, we present a simple metaheuristic approach based on the Iterated Local Search (ILS) algorithm which has been applied with great success to the related problems. Extensive comparative evaluations are carried out against the state-of-the-art techniques for the problem in the literature. The computational results show that ILS can detect communities with high quality and stability.
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