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Processes 2019, 7(2), 111; https://doi.org/10.3390/pr7020111

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.
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)
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Abstract

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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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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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.

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