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

• Published in: IEEE transactions on cybernetics Vol. 50; no. 2; pp. 703 - 716
• Main Authors: Zhang, Xingyi, Zhou, Kefei, Pan, Hebin, Zhang, Lei, Zeng, Xiangxiang, Jin, Yaochu
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.02.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Community; Community detection; complex network; Complex networks; Density measurement; Detection algorithms; evolutionary algorithm; Evolutionary algorithms; Evolutionary computation; Feature extraction; Genetic algorithms; large-scale network; Maintenance; multiobjective optimization; Multiple objective analysis; Network reduction; Networks; Nodes; Optimization; Scalability
• ISSN: 2168-2267; 2168-2275
• DOI: 10.1109/TCYB.2018.2871673

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.