LAPSE:2019.0601
Published Article
LAPSE:2019.0601
Parallel Conical Area Community Detection Using Evolutionary Multi-Objective Optimization
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.
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Keywords
community detection, complex networks, evolutionary algorithms, multi-objective optimization, parallel island models
Subject
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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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
Other Meta
PII: pr7020111, Publication Type: Journal Article
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Published Article
LAPSE:2019.0601
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External Link
doi:10.3390/pr7020111
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Version History
[v1] (Original Submission)
Jun 18, 2019
Verified by curator on
Jun 18, 2019
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v1
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https://psecommunity.org/LAPSE:2019.0601
Original Submitter
Calvin Tsay
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