Community detection in complex networks using immune discrete differential evolution algorithm

被引：0
|
作者：
Zhang, Ying-Jie [1 ]
Gong, Zhong-Han [1 ]
Chen, Qian-Kun [1 ]
机构：
[1] College of Information Science and Engineering, Hunan University, Changsha
来源：
Zidonghua Xuebao/Acta Automatica Sinica | 2015年 / 41卷 / 04期
基金：
中国国家自然科学基金;
关键词：
Clonal selection; Community detection; Differential evolution; Modularity;
D O I：
10.16383/j.aas.2015.c140018
中图分类号：
学科分类号：
摘要：
Aimed at the existing problem of community detection in complex networks, a novel immune discrete differential evolution (IDDE) is proposed in the framework of standard differential evolution. In the proposed method, the initial population is generated through label propagation, and the discrete differential evolution strategy is utilized to ensure the global searching ability of the IDDE; meanwhile, the high-frequency clonal selection mutation operation is applied to excellent individuals of the population to improve the local exploitation ability and the convergence performance of the IDDE. Artificial networks and several real networks are employed to test the performance of the IDDE, and the testing results show that the IDDE achieves better searching ability and stronger robustness, and that it can detect the community structure in complex networks effectively. Copyright © 2015 Acta Automatica Sinica. All rights reserved.
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收藏
页码：749 / 757
页数：8
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