A Network Reduction-Based Multiobjective Evolutionary Algorithm for Community Detection in Large-Scale Complex Networks

作     者：Zhang, Xingyi Zhou, Kefei Pan, Hebin Zhang, Lei Zeng, Xiangxiang Jin, Yaochu 

作者机构：Anhui Univ Sch Comp Sci & Technol Minist Educ Key Lab Intelligent Comp & Signal Proc Hefei 230039 Peoples R China Xiamen Univ Dept Comp Sci Xiamen 361005 Peoples R China Univ Surrey Dept Comp Sci Guildford GU2 7XH Surrey England 

出 版 物：《IEEE TRANSACTIONS ON CYBERNETICS》 (IEEE Trans. Cybern.)

年 卷 期：2020年第50卷第2期

页      面：703-716页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Natural Science Foundation of China [61822301, 61672033, 61502001, 61876184, 61502004] Anhui Provincial Natural Science Foundation for Distinguished Young Scholars [1808085J06] Joint Research Fund for Overseas Chinese, Hong Kong and Macao Scholars of the National Natural Science Foundation of China U.K. EPSRC [EP/M017869/1] EPSRC [EP/M017869/1] Funding Source: UKRI 

主　　题：Complex networks Detection algorithms Optimization Feature extraction Evolutionary computation Density measurement Scalability Community detection complex network evolutionary algorithm large-scale network multiobjective optimization 

摘      要：Evolutionary algorithms have been demonstrated to be very competitive in the community detection for complex networks. They, however, show poor scalability to large-scale networks due to the exponential increase of search space. In this paper, we suggest a network reduction-based multiobjective evolutionary algorithm for community detection in large-scale networks, where the size of the networks is recursively reduced as the evolution proceeds. In each reduction of the network, the local communities found by the elite individuals in the population are identified as nodes of the reduced network for further evolution, thereby considerably reducing the search space. A local community repairing strategy is also suggested to correct the misidentified nodes after each network reduction during the evolution. Experimental results on synthetic and real-world networks demonstrate the superiority of the proposed algorithm over several state-of-the-art community detection algorithms for large-scale networks, in terms of both computational efficiency and detection performance.