Multiresolution Community Detection Based on Fuzzy Clustering

被引：3

作者：

Wang X. ^{[1
]}

Liu G. ^{[1
]}

Li J. ^{[1
]}

机构：

[1] School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai

来源：

Dianzi Yu Xinxi Xuebao/Journal of Electronics and Information Technology | 2017年 / 39卷 / 09期

基金：

中国国家自然科学基金;

关键词：

Community structure; Fuzzy clustering; Social network; Structural similarity;

D O I：

10.11999/JEIT161116

中图分类号：

学科分类号：

摘要：

Focusing on the complexity of network structure and the indeterminacy of community partition, this paper puts forward a novel fuzzy clustering method for uncovering community structures. In contrast to previous studies, the proposed method disposes the similarity of connecting vertices with fuzzy relation. Based on local interactive information, it considers the fuzzy relation between vertices and the transitive similarity in network topology to divide vertices into communities. In addition, multiresolution communities can be detected by adjusting fuzzy parameter. In order to avoid subjectivity in the selection of cluster number, a new modularity is introduced to evaluate the effectiveness of the clustering analysis. It is proved by experiments that the method is ef?cient and stable to detect underlying communities. © 2017, Science Press. All right reserved.

引用

页码：2033 / 2039

页数：6

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