The usual community detection methods are not applicable to bipartite networks due to their special 2-mode structure.To identifying the community structure of bipartite networks,this paper proposed a novel algorithm based on KullbackLeibler (KL) divergence between the 2-mode nodes.According to the connecting conditions between user set and object set,the algorithm obtained the link probability distribution on user set of bipartite networks,and developed KL similarity as a mettic to evaluate the difference of node link patterns,and then detected the communities in bipartite networks overcoming the limitation of the 2-mode structure on nodes clustering.The experimental results and analysis in compute-generated and real network all show that this algorithm can effectively mine the meaningful community structures in bipartite networks,and improves the performance of community identification in the accuracy and efficiency.%由于二分网络特殊的二分结构,使得基于单模网络的现有社区发现算法无法适用.提出一种基于Kullback-Leibler距离的二分网络社区发现算法,该算法将异质节点间的连接关系转换为其在用户节点集上的连接概率分布,并建立基于概率分布的KL相似度衡量节点连接模式的差异性,从而克服二分结构对节点相似性评估的不利影响,实现对二分网络异质节点的社区发现.在人工网络和真实网络上的实验和分析表明,该算法能够有效挖掘二分网络社区结构,改善二分网络社区发现的准确性和效率.
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