基于 Kullback-Leibler 距离的二分网络社区发现方法.

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 Kullback-Leibler (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 K L similarity as a metric 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. [ABSTRACT FROM AUTHOR]