An Algorithm Q-PSO for Community Detection in Complex Networks

被引：4
|
作者：
Cai, Xiaohong [1 ]
Shi, Yuan [1 ]
Zhu, Youze [1 ]
Qiao, Yulu [1 ]
Hu, Fang [1 ]
机构：
[1] Hubei Univ Chinese Med, Coll Informat Engn, Wuhan 430065, Hubei, Peoples R China
来源：
2017 16TH INTERNATIONAL SYMPOSIUM ON DISTRIBUTED COMPUTING AND APPLICATIONS TO BUSINESS, ENGINEERING AND SCIENCE (DCABES) | 2017年
关键词：
Q-PSO Algorithm; Community Detection; Modularity; Normal Mutual Information; Simulation Experiment; OPTIMIZATION;
D O I：
10.1109/DCABES.2017.23
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
In this paper, based on the particle swarm optimization (PSO) algorithm, introducing the idea of modularity function optimization, a new algorithm Q-PSO for detecting community is proposed. This algorithm can identify the community structure accurately and effectively. In order to verify the performance of this algorithm, which is tested on several representative real-world networks and a set of computer-generated networks based on LFR-benchmark. The experimental results demonstrated that this algorithm can identify the communities accurately, and compared with CNM, Walktrap and infomap algorithms, the presented algorithm can acquire higher values of modularity and NMI in most networks.
引用
收藏
页码：76 / 79
页数：4
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