Distributed Community Detection in Complex Networks

Network analysis is an important and interesting area of research with many applications in different domains. One of the challenges in network analysis is community detection. Community detection is the process of partitioning the network into some groups in such a way that there exist many interactions in the groups and few interactions among them. Toward improving time complexity and precision of community detection, a novel method is proposed in this paper. This method is able to detect multiple communities simultaneously. No prior knowledge is required about the number of communities or the structure of network in proposed algorithm. This algorithm is evaluated by modularity measure on different networks and the result shows improvements over existing methods. We use local modularity as the similarity measurement to collect similar nodes in one community. Our method is a kind of agglomerative approach.