Community Detection by Fuzzy Relations

被引：30

作者：

Luo, Wenjian ^{[1
,2
]}

Yan, Zhenglong ^{[1
,2
]}

Bu, Chenyang ^{[1
,2
]}

Zhang, Daofu ^{[1
,2
]}

机构：

[1] Univ Sci & Technol China, Sch Comp Sci & Technol, Hefei 230027, Anhui, Peoples R China

[2] Univ Sci & Technol China, Anhui Prov Key Lab Software Engn Comp & Commun, Hefei 230027, Anhui, Peoples R China

来源：

IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING | 2020年 / 8卷 / 02期

关键词：

Detection algorithms; Clustering algorithms; Image edge detection; Social network services; Partitioning algorithms; Sun; Complexity theory; Social network; community detection; fuzzy relation; ALGORITHM;

D O I：

10.1109/TETC.2017.2751101

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The increasing demand for knowledge from network data poses significant challenges in many tasks. Discovering community structure from a network is one of the classic and significant problems faced in network analysis. In this paper, we study the network structure from the perspective of the composition of fuzzy relations, and a novel algorithm based on fuzzy relations, i.e., CDFR (Community Detection by Fuzzy Relations), is proposed for non-overlapping community detection. The key idea of CDFR is to find the NGC node (Nearest node with Greater Centrality) for each node and compute the fuzzy relation between them. Then, the community to which a node belongs depends on its NGC node. In addition, the decision graph will be constructed to guide community detection. Experimental results on artificial and real-world networks verify the effectiveness and superiority of our CDFR algorithm.

引用

页码：478 / 492

页数：15

共 52 条

[1]

[Anonymous], SOC NETW

[2]

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[3] AN ALGORITHM FOR PARTITIONING THE NODES OF A GRAPH [J].