Enhancing betweenness algorithm for detecting communities in complex networks

被引：3

作者：

Chen, Benyan ^{[1
]}

Xiang, Ju ^{[2
]}

Hu, Ke ^{[3
,4
]}

Tang, Yi ^{[3
,4
]}

机构：

[1] Xiangtan Univ, Dept Phys, Xiangtan 411105, Hunan, Peoples R China

[2] Changsha Med Univ, Dept Comp Sci, Changsha 410219, Hunan, Peoples R China

[3] Xiangtan Univ, Hunan Key Lab Micro Nano Energy Mat & Devices, Xiangtan 411105, Hunan, Peoples R China

[4] Xiangtan Univ, Lab Quantum Engn & Micro Nano Energy Technol, Xiangtan 411105, Hunan, Peoples R China

来源：

MODERN PHYSICS LETTERS B | 2014年 / 28卷 / 09期

基金：

中国国家自然科学基金;

关键词：

Complex network; community structure; edge betweenness; ORGANIZATION; IDENTIFICATION; MODULARITY;

D O I：

10.1142/S0217984914500742

中图分类号：

O59 [应用物理学];

学科分类号：

摘要：

Community structure is an important topological property common to many social, biological and technological networks. First, by using the concept of the structural weight, we introduced an improved version of the betweenness algorithm of Girvan and Newman to detect communities in networks without (intrinsic) edge weight and then extended it to networks with (intrinsic) edge weight. The improved algorithm was tested on both artificial and real-world networks, and the results show that it can more effectively detect communities in networks both with and without (intrinsic) edge weight. Moreover, the technique for improving the betweenness algorithm in the paper may be directly applied to other community detection algorithms.

引用

页数：11

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