摘要
提出一种基于最小熵的社团结构检测算法。首先用模糊关系表示交互网络,一种基于熵的测度来确定模糊关系的隶属度,且熵越小,节点越相似。然后提出一种新的模糊关系合成规则,通过应用该规则,模糊关系被转换为最小关系。最后,通过熵的值把这个最小模糊关系划分成一个个社团。在人工网络与真实网络中的测试结果表明,该算法可以有效地识别社团结构。
This paper proposes an efficient method based on minimum entropy for community detection in complex networks. First, an interaction network is denoted by a fuzzy relation, and the entropy is proposed to determine the membership grade of the relation, and the lower the entropy, the more similar the vertices. Then, the fuzzy re- lation is transformed into a minimal fuzzy relation by a novel composition rule of fuzzy relation that is presented here. Finally, this minimal fuzzy relation is partitioned into clusters based on the value of entropy. The results both in ar- tificial networks and real-world networks show that our method is efficient in community detection.
出处
《电子科技》
2012年第3期13-16,20,共5页
Electronic Science and Technology
关键词
社团结构
模糊关系
熵
聚类
community structure
fuzzy relation
entropy
clustering
