An efficient multiobjective evolutionary algorithm for community detection in social networks

Community detection in complex networks has been addressed in different ways recently. To identify communities in social networks we can formulate it with two different objectives, maximization of internal links and minimization of external links. Because these two objects are correlated, the relationship between these two objectives is a trade-off. This study employed harmony search algorithm, which was conceptualized using the musical process of finding a perfect state of harmony to perform this bi-objective trade-off. In the proposed algorithm an external repository considered to save non-dominated solutions found during the search process and a fuzzy clustering technique is used to control the size of repository. The harmony search algorithm was applied on well-known real life networks, and good Pareto solutions were obtained when compared with other algorithms, such as the MOGA-Net and Newman algorithms.