摘要
现有算法虽然能发现二模网络的社区结构,但由于实际网络的多样性或复杂性,往往不能预知社区个数及相关信息,无法相对准确地发现真实的社区结构.针对此问题,文中提出自主确定社区个数的二模网络社区发现算法——聚类分配算法(CAA).该算法有效利用二模网络中两类节点的交互信息,解决确定社区个数的难题.对网络中的T类节点进行聚类,再将B类节点按照某种分配机制分配到已有类中.实验表明,CAA比基于资源分布矩阵的算法和基于边集聚系数的算法有更高的准确性,能获得更高质量的社区划分.
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 theexisting 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.
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2015年第11期969-975,共7页
Pattern Recognition and Artificial Intelligence
基金
国家优秀青年基金项目(No.61322211)
教育部新世纪人才支持计划项目(No.NCET-12-1031)
教育部博士点专项科研基金项目(No.20121401110013)
山西省青年学术带头人项目(No.20120301)资助
关键词
二模网络
社区挖掘
聚类分配算法
模块度
Bipartite Network, Community Mining, Cluster-Assign Algorithm, Modularity
