Multi-objective NSGA-II based community detection using dynamical evolution social network

Community detection is becoming a highly demanded topic in social networking-based applications. It involves finding the maximum intraconnected and minimum inter-connected sub-graphs given networks. Many approaches have been developed for community’s less of them focused on dynamical aspect network. The decision community has to consider pattern changes network be smooth enough. This enable operation other dependent application. Unlike Algorithms, this article presents non-dominated aware searching Algorithm designated as sorting based with awareness (NDS-CD-DA). uses genetic algorithm NSGA-II two objectives: modularity normalized mutual information (NMI). Experimental results synthetic networks real-world datasets compared classical single objective shown provide superiority terms domination well convergence. NDS-CD-DA accomplished percentage 100% over dynamic evolutionary DECS almost all iterations.