An efficient modularity based algorithm for community detection in social network

Community detection process intends to detect clusters in a social network (SN), where nodes within the cluster are densely connected as compared to nodes outside the cluster. This process is one of the challenging issues in era of big data analytics particularly in the area of social networking. Graph data structure is often used to represent SN, where nodes can be used to represent actors and edges can be used to represent relationships among the actors. There are several algorithms for community detection purpose in a SN but each one has certain drawbacks in detecting community over a large scale network. In this paper an efficient modularity based community detection algorithm has been proposed. The proposed algorithm has been compared with other existing community detection algorithms using some of the most popular social network datasets. Performance of the algorithm has been assessed using various parameters like modularity, clustering coefficient, execution time etc.