Community Detection in Signed Social Networks Using Multiobjective Genetic Algorithm

Clustering of like-minded users is basically the goal of community detection (CD) in social networks and many researchers have proposed different algorithms for the same. In signed social networks (SSNs) where type of link is also considered besides the links itself, CD aims to partition the network in such a way to have less positive inter-connections and less negative intra-connections among communities. So, approaches used for CD in unsigned networks do not perform well when directly applied on signed networks. Most of the CD algorithms are based on single objective optimization criteria of optimizing modularity which focuses only on link density without considering the type of links existing in the network. In this work, a multiobjective approach for CD in SSNs is proposed considering both the link density as well as the sign of links. Precisely we are developing a method using modularity, frustration and social balance factor as multiple objectives to be optimized (M-F-SBF model). NSGA-II algorithm is used to maintain elitism and diversity in the solutions. Experiments are performed on both existing benchmarked and real-world datasets show that our approach has led to better solutions, clearly indicating the effectiveness of our proposed M-F-SBF model.