A local Random Walk method for identifying communities in social networks

Community detection is a substantial technique to find out the relationship between nodes in complex networks. By understanding the behavior of elements in a community, one can predict the overall feature of the large scale social network. Detecting different communities in large scale network is a challenging task due to huge data size associated with such network. The main purpose of this paper is finding distinct communities. For this reason, in this paper after using limited Random Walk to detect nodes feature set, nodes that share higher common feature set form a community. Experimental results in real and artificial networks show, with great accuracy, that the proposed method succeeds to recover communities in the network.