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Open AccessArticle

Parallel Conical Area Community Detection Using Evolutionary Multi-Objective Optimization

1
School of Software Engineering, South China University of Technology, Guangzhou 510006, China
2
School of Computer Science and Educational Software, Guangzhou University, Guangzhou 510006, China
3
School of Statistics, Renmin University of China, Beijing 100872, China
*
Authors to whom correspondence should be addressed.
Processes 2019, 7(2), 111; https://doi.org/10.3390/pr7020111
Received: 21 December 2018 / Revised: 10 February 2019 / Accepted: 16 February 2019 / Published: 20 February 2019
(This article belongs to the Special Issue Process Systems Engineering à la Canada)
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. View Full-Text
Keywords: complex networks; community detection; multi-objective optimization; evolutionary algorithms; parallel island models complex networks; community detection; multi-objective optimization; evolutionary algorithms; parallel island models
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MDPI and ACS Style

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.

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