A parallel multi-objective evolutionary algorithm for community detection in large-scale complex networks

作     者：Su, Yansen Zhou, Kefei Zhang, Xingyi Cheng, Ran Zheng, Chunhou 

作者机构：Anhui Univ Sch Artif Intelligence Key Lab Intelligent Comp Signal Proc Minist Educ Hefei 230601 Peoples R China Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen Key Lab Computat Intelligence Shenzhen 518055 Peoples R China 

出 版 物：《INFORMATION SCIENCES》 (信息科学)

年 卷 期：2021年第576卷

页      面：374-392页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Key Project of Science and Technology Innovation - Ministry of Science and Technology of China [2018AAA0101302] National Natural Science Foundation of China [61672033, 61822301, U1804262] Anhui Provincial Natural Science Foundation for Distinguished Young Scholars [1808085J06] Recruitment Program for Leading Talent Team of Anhui Province [2019-16] Key Program of Natural Science Project of Educational Commission of Anhui Province [KJ2019A0029] National Key R&D Program of China [2017YFC0804 003] Program for Guangdong Introducing Innovative and Entrepreneurial Teams [2017ZT07X386] Shenzhen Peacock Plan [KQTD201611 2514355531] 

主　　题：Evolutionary algorithm Multi-objective optimization Community detection Complex network Parallel algorithm 

摘      要：Community detection in large-scale complex networks has recently received significant attention as the volume of available data is becoming larger. The use of evolutionary algorithms (EAs) for community detection in large-scale networks has gained considerable popularity because these algorithms are fairly effective in networks with a relatively small number of nodes. In this paper, we propose a parallel multi-objective EA, called PMOEA, for community detection in large-scale networks, where the communities associated with key network nodes are detected in parallel. Specifically, we develop a multi-objective and a single-objective EA. The former is used to detect the communities of a key node instead of all communities in the network. The latter obtains the communities in the entire network using the previously detected communities of each key node. The performance of the proposed method was verified on both large-scale synthetic benchmark networks and real-world networks. The results demonstrated the superiority of PMOEA over six EA-based and two non-EA-based community-detection algorithms for large-scale networks. (c) 2021 Elsevier Inc. All rights reserved.