An Efficient Algorithm for Community Detection in Complex Networks

Community structure detection in complex networks attracts considerable attention in recent years. In this paper we propose an algorithm to detect community structures in very large networks. Basing on the local community detection, this algorithm is able to detect global community structures. We found that the local maximal degree nodes locate dispersedly in networks and can be considered as key nodes of communities. By discovering local communities iteratively from local maximal degree nodes, the global community structures are identified. Only local graph information is required to discover the local maximal degree nodes and their local communities. No priori information (e.g. topology structure of the entire network, the number and sizes of the communities) is needed. The local communities can be detected in parallel by starting simultaneously from several local maximal degree nodes. Comparing to other community detection methods, our algorithm is time efficient and suitable to finding community structures in large real networks.