关键词: 社团结构, 模糊关系, 熵, 聚类

Abstract:

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 relation 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 artificial networks and real-world networks show that our method is efficient in community detection.

Key words: community structure;fuzzy relation;entropy;clustering