标题:An iterated local search algorithm for community detection in complex networks
作者:Liu C.; Kang Q.; Kong H.; Li W.; Kang Y.
作者机构:[Liu, C] School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai, No. 180 Wenhua Road, Weihai, Shandong, 264209, Chi 更多
通讯作者:Kang, Q(qmkang@sdu.edu.cn)
通讯作者地址:[Kang, Q] School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai, No. 180 Wenhua Road, China;
来源:International Journal of Modern Physics B
出版年:2020
卷:34
期:4
DOI:10.1142/S0217979220500137
关键词:Community detection; complex network; iterated local search; metaheuristics; modularity
摘要:Community detection is one of the most challenging problems in complex network analysis. This problem attracts an amount of interest from various scientific fields such as biology, social network and physics. In the past few decades, numerous heuristics and exact algorithms have been designed to address the problem. However, most of them are not suitable for large networks, since they require considerable computing time. Contrary to the recent trend in the community detection literature, where complex nature-inspired methods are often proposed, we present a simple metaheuristic approach based on the Iterated Local Search (ILS) algorithm which has been applied with great success to the related problems. Extensive comparative evaluations are carried out against the state-of-the-art techniques for the problem in the literature. The computational results show that ILS can detect communities with high quality and stability. © 2020 World Scientific Publishing Company.
收录类别:SCOPUS
资源类型:期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078054348&doi=10.1142%2fS0217979220500137&partnerID=40&md5=34b5ba2b581423aa23c0afb116eeab31
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