Algorithm of Detecting Community in Bipartite Network with Autonomous Determination of the Number of Communities
GUO Gai-Gai1, QIAN Yu-Hua2,3, ZHANG Xiao-Qin1,3, LI Ye-Bin2
1.School of Mathematics Sciences, Shanxi University, Taiyuan 030006 2.Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Shanxi University, Taiyuan 030006 3.Institute of Intelligent Information Processing, Shanxi University, Taiyuan 030006
Abstract：The existing algorithms can find the community structure in bipartite network. However, they can not predict the number of communities and the relevant information and discover the real community structure accurately due to the variety and the complexity of the real network. In this paper, an algorithm of detecting community structure in bipartite network-cluster assign algorithm (CAA) is proposed and it determines the number of communities autonomously. In this algorithm, the interaction information between two types of nodes is used effectively and the problem of determining the number of communities is solved. The T-type nodes of the network are clustered, then the B-type nodes are assigned to the existing classes according to the allocation mechanism. Experiments show CAA obtains a higher quality community and has a higher accuracy than the algorithms based on resource distribution matrix and edge cluster coefficient.
郭改改，钱宇华，张晓琴，李烨斌. 自主确定社区个数的二模网络社区发现算法*[J]. 模式识别与人工智能, 2015, 28(11): 969-975.
GUO Gai-Gai, QIAN Yu-Hua, ZHANG Xiao-Qin, LI Ye-Bin. Algorithm of Detecting Community in Bipartite Network with Autonomous Determination of the Number of Communities. , 2015, 28(11): 969-975.
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