Parallel Conical Area Community Detection Using Evolutionary Multi-Objective Optimization
AbstractDetecting community structures helps to reveal the functional units of complex networks. In this paper, the community detection problem is regarded as a modularity-based multi-objective optimization problem (MOP), and a parallel conical area community detection algorithm (PCACD) is designed to solve this MOP effectively and efficiently. In consideration of the global properties of the selection and update mechanisms, PCACD employs a global island model and targeted elitist migration policy in a conical area evolutionary algorithm (CAEA) to discover community structures at different resolutions in parallel. Although each island is assigned only a portion of all sub-problems in the island model, it preserves a complete population to accomplish the global selection and update. Meanwhile the migration policy directly migrates each elitist individual to an appropriate island in charge of the sub-problem associated with this individual to share essential evolutionary achievements. In addition, a modularity-based greedy local search strategy is also applied to accelerate the convergence rate. Comparative experimental results on six real-world networks reveal that PCACD is capable of discovering potential high-quality community structures at diverse resolutions with satisfactory running efficiencies. View Full-Text
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Ying, W.; Jalil, H.; Wu, B.; Wu, Y.; Ying, Z.; Luo, Y.; Wang, Z. Parallel Conical Area Community Detection Using Evolutionary Multi-Objective Optimization. Processes 2019, 7, 111.
Ying W, Jalil H, Wu B, Wu Y, Ying Z, Luo Y, Wang Z. Parallel Conical Area Community Detection Using Evolutionary Multi-Objective Optimization. Processes. 2019; 7(2):111.Chicago/Turabian Style
Ying, Weiqin; Jalil, Hassan; Wu, Bingshen; Wu, Yu; Ying, Zhenyu; Luo, Yucheng; Wang, ZhenYu. 2019. "Parallel Conical Area Community Detection Using Evolutionary Multi-Objective Optimization." Processes 7, no. 2: 111.
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