Journal
IEEE TRANSACTIONS ON CYBERNETICS
Volume 50, Issue 2, Pages 703-716Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2018.2871673
Keywords
Complex networks; Detection algorithms; Optimization; Feature extraction; Evolutionary computation; Density measurement; Scalability; Community detection; complex network; evolutionary algorithm; large-scale network; multiobjective optimization
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Funding
- 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 [61428302]
- U.K. EPSRC [EP/M017869/1]
- EPSRC [EP/M017869/1] Funding Source: UKRI
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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.
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