Finding Community Structure in Complex Networks Using Parallel Approach

Network analysis is an important term in different scientific areas and finding the structure of communities is a significant challenge in network analysis. A group of vertices with high intra-connection and sparse inter-connection is called community. In this paper, we propose a novel method for community detection in networks, which works better in time and precision compared to similar methods. The proposed method is able to detect communities of a wide variety of networks with different properties. This method is an agglomerative parallel algorithm. Also it can find multiple communities and exchange the nodes between detected communities simultaneously. It has utilized local modularity for constructing the communities. After all, genetic algorithm is used to optimize the parameters of the proposed method. The algorithm is evaluated by modularity metric and shows a noticeable good precision. Also it has used simulated annealing to maximize the modularity.