Community detection in complex networks: Multi-objective discrete backtracking search optimization algorithm with decomposition |
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| Affiliation: | 1. School of Physics and Electronic Information, Huaibei Normal University, Huaibei, 235000, China;2. School of Automation, Guangdong University of Technology, Guangzhou, 510006, China;3. Beijing Information Science and Technology University, Beijing 100192, China;1. College of Computer Science and Technology, Jilin University, Changchun, Jilin 130012, China;2. Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin 130012, China;3. College of Mathematics, Jilin University, Changchun, Jilin 130012, China;1. Department of Electronic Engineering, Shanghai Jiao Tong University, 800 Dong Chuan Road, Shanghai, China;2. National Engineering Laboratory for Information Content Analysis Technology, Shanghai Jiao Tong University, Shanghai, China;1. Department of Computer Science, University of Baghdad, Iraq;2. Department of Computer Science, University of Bahrain, Bahrain;1. Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Xi’an, China;2. School of Mechanical Engineering, University of Birmingham, UK;1. College of Computer Science and Technology, Harbin Engineering University, Heilongjiang 150001, China;2. College of Computer Science and Technology, Harbin University of Science and Technology, Heilongjiang 150001, China |
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| Abstract: | Community detection is believed to be a very important tool for understanding both the structure and function of complex networks, and has been intensively investigated in recent years. Community detection can be considered as a multi-objective optimization problem and the nature-inspired optimization techniques have shown promising results in dealing with this problem. In this study, we present a novel multi-objective discrete backtracking search optimization algorithm with decomposition for community detection in complex networks. First, we present a discrete variant of the backtracking search optimization algorithm (DBSA) where the updating rules of individuals are redesigned based on the network topology. Then, a novel multi-objective discrete method (MODBSA/D) based on the proposed discrete variant DBSA is first proposed to minimize two objective functions in terms of Negative Ratio Association (NRA) and Ratio Cut (RC) of community detection problems. Finally, the proposed algorithm is tested on some real-world networks to evaluate its performance. The results clearly show that MODBSA/D has effective and promising performance for dealing with community detection in complex networks. |
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| Keywords: | Community detection Backtracking search optimization Discrete Multi-objective optimization Decomposition |
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