计算机应用研究2017，Vol.34Issue(5)：1480-1483,1486,5.DOI:10.3969/j.issn.1001-3695.2017.05.046
基于Kullback-Leibler距离的二分网络社区发现方法
Algorithm of identifying community in bipartite networks based on Kullback-Leibler divergence
摘要
Abstract
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.关键词
社区发现/二分网络/连接模式/Kullback-Leibler距离Key words
community detection/bipartite network/link pattern/Kullback-Leibler divergence分类
信息技术与安全科学引用本文复制引用
张皓,王明斐,陈艳浩..基于Kullback-Leibler距离的二分网络社区发现方法[J].计算机应用研究,2017,34(5):1480-1483,1486,5.基金项目
河南省高等学校重点科研资助项目(15A520063,16A520083) （15A520063,16A520083）
