A Spin-Glass Model Based Local Community Detection Method in Social Networks

Mining community structures has become a general problem which exists in many fields including: Computer-Science, Mathematics, Physics, Biology, Sociology and so on. It has developed rapidly and been used widely in many applications: web data mining, social network analysis, criminal network mining, protein interaction network analysis, metabolic network analysis, genetic network analysis, customers relationship mining and user online behavior analysis, etc. Most community detection algorithms try to obtain the global information of the network, but increasing large scale of the current network makes it computationally expensive. In the meanwhile, the different influence and different behavior of nodes in the network are ignored. In fact, if we know the local information of the network or the interested node, we can easily detect the local community. This paper proposes a multi-resolution local community detection algorithm named MRCDA which uses local structural information in the network to optimize the multi-resolution modularity based on the Potts spin-glass model. A local community can be detected through continuous optimization of the function by expanding from an initial influential node computed by a modified PageRank sorting algorithm. The proposed MRCDA has been tested on both synthetic and real world networks and tested against other algorithms. The experiments demonstrate its efficiency and accuracy.