Graph reconstruction and attraction method for community detection

Community detection as one of the hot issues in complex networks has attracted a large amount of attention in the past several decades. Although many methods perform well on this problem, they become incapable if the networks exhibit more complicated characteristics, e.g. strongly overlapping communities. This paper explores a graph reconstruction and attraction method (GRAM) for community detection. In GRAM, we extract network structure information of a graph by introducing a new passing probability matrix based on Markov Chains by which a new graph is further reconstructed, and modularity optimization is adopted on the reconstructed one instead of the original one for non-overlapping community detection. For identifying overlapping communities, we first initialize a cluster with a vital node as an origin of attraction, then the cluster is extended by graph attraction based on the passing probability. This procedure is repeated for the remaining nodes, and each isolated node if exists is finally classified into its most attractable cluster. Experiments on artificial and real-world datasets have shown the superiority of the proposed method for community detection particularly on the datasets with even more complex, sparse and ambiguous network structures.