基于信息扩散概率的二分网络社区划分算法

二分网络是复杂网络的表现形式之一,二分网络单侧节点的社区划分对研究复杂网络具有重要的实际意义.基于信息在网络中的扩散概率和模块度思想,本文提出了一个针对二分网络的社区划分聚类算法(IPS算法).该算法通过模拟信息在网络中扩散的过程,利用各个节点的信息量在网络中扩散后,每个节点收到其他节点的信息量作为社区之间合并的依据,并引入二分网络模块度作为社区划分优劣判断的依据.最后算法在典型网络上测试结果表明,该算法不仅能够精确的识别二分网络社区个数,而且可以获得高质量的社区划分结果.

Community Division of Bipartite Network Based on Information Transfer Probability

Bipartite network is a performance of complex networks, the divided of unilateral node of bipartite network has important practical significance for the study of complex networks of community division. Based on the idea of information transfer probability, this paper presents a community of bipartite network divided clustering algorithm (IPS algorithm). The algorithm simulates the probability of information transfer in the network, through mutual support value between the nodes in network.select the max value as the basis for merger different communities and introduction of bipartite network module as a basis for judging the merits of community division. Finally, we using actual network test the performance of the algorithm. Experimental results show that the algorithm can not only accurate divided the unilateral node of bipartite network, but also can get high quality community division.