Large-scale community detection based on node membership grade and sub-communities integration

Community detection plays an important role in research on network characteristics and in the mining of network information. A variety of algorithms have previously been proposed, but with the continuous growth of network scale, few of them can detect community structure efficiently. Additionally, most of these algorithms only consider non-overlapping community structures in networks. This paper addresses these problems by proposing a new algorithm, based on node membership grade and sub-communities integration, to detect community structure in large-scale networks. The proposed algorithm firstly introduces two functions based on the local information of each node in networks, namely neighboring inter-nodes membership function fMS−NN and node-to-community membership function fMS−NC. Firstly, local potential’s complete sub-graphs are efficiently mined using the function fMS−NN, and then these small graphs are merged into larger ones in light of local modularity. Secondly, incorrectly divided nodes are modified according to function fMS−NN. Additionally, by adjusting the parameters in fMS−NC, we can accurately obtain both non-overlapping communities and overlapping communities. Furthermore, the proposed algorithm employs a framework resembling label propagation, which has low time complexity and is suitable for detecting communities in large-scale networks. Experimental results on both artificial networks and real networks indicate the accuracy and efficiency of the proposed algorithm.