Distributed parallel deep learning with a hybrid backpropagation-particle swarm optimization for community detection in large complex networks
In this paper, a parallel deep learning-based community detection method in large complex networks (CNs) is proposed. First, a CN partitioning method is employed to divide the CN into multiple chunks to improve the efficiency in terms of space and time complexities. Next, the method is integrated wi...
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| Published in: | Information sciences Vol. 600; pp. 94 - 117 |
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| Language: | English |
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01.07.2022
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| ISSN: | 0020-0255, 1872-6291 |
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| Abstract | In this paper, a parallel deep learning-based community detection method in large complex networks (CNs) is proposed. First, a CN partitioning method is employed to divide the CN into multiple chunks to improve the efficiency in terms of space and time complexities. Next, the method is integrated with two optimization algorithms: (1) backpropagation (BP), which optimizes deep learning locally within each local chunk of the CN; (2) particle swarm optimization (PSO), which is used to improve the BP optimization involving all CN chunks. PSO utilizes a multi-objective function to improve the effectiveness of the proposed method. In addition, a distributed environment is set up to conduct parallel optimization of the proposed method so that multi-local optimizations could be performed simultaneously. A set of 16 real-world CNs in a range from small to large size are used to verify the effectiveness and efficiency of the method in a benchmark study. The proposed method is implemented in multi-machines with central processing unit (CPU) and graphics processing unit (GPU) devices. The results reveal the effective role of the proposed deep learning with hybrid BP–PSO optimization in detecting communities in large CNs, which requires minimum execution time on both CPU and GPU devices. |
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| AbstractList | In this paper, a parallel deep learning-based community detection method in large complex networks (CNs) is proposed. First, a CN partitioning method is employed to divide the CN into multiple chunks to improve the efficiency in terms of space and time complexities. Next, the method is integrated with two optimization algorithms: (1) backpropagation (BP), which optimizes deep learning locally within each local chunk of the CN; (2) particle swarm optimization (PSO), which is used to improve the BP optimization involving all CN chunks. PSO utilizes a multi-objective function to improve the effectiveness of the proposed method. In addition, a distributed environment is set up to conduct parallel optimization of the proposed method so that multi-local optimizations could be performed simultaneously. A set of 16 real-world CNs in a range from small to large size are used to verify the effectiveness and efficiency of the method in a benchmark study. The proposed method is implemented in multi-machines with central processing unit (CPU) and graphics processing unit (GPU) devices. The results reveal the effective role of the proposed deep learning with hybrid BP–PSO optimization in detecting communities in large CNs, which requires minimum execution time on both CPU and GPU devices. |
| Author | Nasser Al-Andoli, Mohammed Chiang Tan, Shing Ping Cheah, Wooi |
| Author_xml | – sequence: 1 givenname: Mohammed surname: Nasser Al-Andoli fullname: Nasser Al-Andoli, Mohammed organization: Faculty of Information Science and Technology, Multimedia University, Jalan Ayer Keroh Lama, Bukit Beruang, Melaka, Malaysia – sequence: 2 givenname: Shing surname: Chiang Tan fullname: Chiang Tan, Shing email: sctan@mmu.edu.my organization: Faculty of Information Science and Technology, Multimedia University, Jalan Ayer Keroh Lama, Bukit Beruang, Melaka, Malaysia – sequence: 3 givenname: Wooi surname: Ping Cheah fullname: Ping Cheah, Wooi organization: School of Computer Science, Faculty of Science and Engineering, University of Nottingham Ningbo China, Taikang East Road, Ningbo 315100 China |
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| Keywords | Deep learning Community detection Backpropagation algorithm Distributed and parallel computing Particle swarm optimization Complex networks |
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| Title | Distributed parallel deep learning with a hybrid backpropagation-particle swarm optimization for community detection in large complex networks |
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