Multiplex community detection in complex networks using an evolutionary approach Abstract   Multiplex networks are the general representative of complex systems composed of distinct interactions between the same entities on multiple layers. Community detection in the multiplex networks is the problem of finding a shared structure under all layers, which combines the information of the entire network. Most of the existing methods for community detection in the singlelayer networks cannot be well applied to detect shared communities in multiplex networks. In this paper, we employ a multiobjective evolutionary approach, namely MultiObjective Evolutionary Algorithm based on Decomposition with Tabu Search (MOEADTS), to detect shared communities in multiplex networks. Also, we have improved the MOEADTS using a social networks analysis measure named Clustering Coefficient (CC) in terms of the generation of the initial population. This hybrid algorithm employs the parallel computing capacity of the MultiObjective Evolutionary Algorithm based on Decomposition (MOEAD) along with the neighborhood search authority of Tabu Search (TS) for discovering Pareto optimal solutions. Extensive experiments on a variety of singlelayer and multiplex realworld data sets show the superiority of the proposed method in comparison to stateoftheart algorithms and its capability for producing improved results.

Evolution of communities in dynamic social networks: An efficient mapbased approach Abstract   The expanded domain of expert system applications has risen the impact of modeling and analysis of community evolution in social networks as an important part of the decisionmaking process. Social networks are timevariant systems, evolving through entities joining or leaving networks and establishing or terminating relationships. In this article, we study evolution of social networks at the level of community structure, by tracking different transformations of communities over time. Upon experimentation, we observed that a considerable portion of community evolution is partial events such as partial merge. Therefore, we define a broader set of community evolution to include partial events. Furthermore, we introduce ICEM, a novel method for Identification of Community Evolution by Mapping. ICEM determines community evolution by tracking community members, implemented with a hashmap. ICEM maps each member to a (t, xe2x80x85c) pair, specifying it is last observed in time window t and community c. We evaluated our proposed approach with seventeen publicly available social network datasets and compared its performance against other wellknown methods in the literature. Our experimental results indicated the performance superiority of our proposed solution. Additionally, we conducted separate comprehensive experiments using three community detection algorithms to highlight the effect of choosing different community discovery methods on community evolution results.