A multi-objective genetic algorithm for community detection in weighted networks

Problem of community detection has attracted many research efforts in recent years. Most of the algorithms developed for this purpose, take advantage of single-objective optimization methods which may be ineffective for complex networks. In addition, most of the networks in the real world are weighted, and therefore, this fact must be of special interest in order to achieve more precise communities in partitioning strategies. Accordingly, in this paper, a community detection method for weighted networks is proposed using multi-objective optimization based on genetic algorithm. Performance evaluation based on experiments on real datasets, shows that considering weights of the edges, leads to higher modularity factor.