LAPSE:2019.0601
Published Article
LAPSE:2019.0601
Parallel Conical Area Community Detection Using Evolutionary Multi-Objective Optimization
Weiqin Ying, Hassan Jalil, Bingshen Wu, Yu Wu, Zhenyu Ying, Yucheng Luo, ZhenYu Wang
June 18, 2019
Detecting 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.
Keywords
community detection, complex networks, evolutionary algorithms, multi-objective optimization, parallel island models
Suggested Citation
Ying W, Jalil H, Wu B, Wu Y, Ying Z, Luo Y, Wang Z. Parallel Conical Area Community Detection Using Evolutionary Multi-Objective Optimization. (2019). LAPSE:2019.0601
Author Affiliations
Ying W: School of Software Engineering, South China University of Technology, Guangzhou 510006, China [ORCID]
Jalil H: School of Software Engineering, South China University of Technology, Guangzhou 510006, China
Wu B: School of Software Engineering, South China University of Technology, Guangzhou 510006, China
Wu Y: School of Computer Science and Educational Software, Guangzhou University, Guangzhou 510006, China [ORCID]
Ying Z: School of Statistics, Renmin University of China, Beijing 100872, China
Luo Y: School of Software Engineering, South China University of Technology, Guangzhou 510006, China
Wang Z: School of Software Engineering, South China University of Technology, Guangzhou 510006, China [ORCID]
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Journal Name
Processes
Volume
7
Issue
2
Article Number
E111
Year
2019
Publication Date
2019-02-20
Published Version
ISSN
2227-9717
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Original Submission
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PII: pr7020111, Publication Type: Journal Article
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LAPSE:2019.0601
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doi:10.3390/pr7020111
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Jun 18, 2019
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Jun 18, 2019
 
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Calvin Tsay
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