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Distributed parallel deep learning with a hybrid backpropagation-particle swarm optimization for community detection in large complex networks. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.03.053] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Wang J, Li H, Liang L, Zhou Y. Community discovery algorithm of complex network attention model. INT J MACH LEARN CYB 2022. [DOI: 10.1007/s13042-021-01471-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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Al-Andoli M, Cheah WP, Tan SC. Deep autoencoder-based community detection in complex networks with particle swarm optimization and continuation algorithms. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021. [DOI: 10.3233/jifs-201342] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Detecting communities is an important multidisciplinary research discipline and is considered vital to understand the structure of complex networks. Deep autoencoders have been successfully proposed to solve the problem of community detection. However, existing models in the literature are trained based on gradient descent optimization with the backpropagation algorithm, which is known to converge to local minima and prove inefficient, especially in big data scenarios. To tackle these drawbacks, this work proposed a novel deep autoencoder with Particle Swarm Optimization (PSO) and continuation algorithms to reveal community structures in complex networks. The PSO and continuation algorithms were utilized to avoid the local minimum and premature convergence, and to reduce overall training execution time. Two objective functions were also employed in the proposed model: minimizing the cost function of the autoencoder, and maximizing the modularity function, which refers to the quality of the detected communities. This work also proposed other methods to work in the absence of continuation, and to enable premature convergence. Extensive empirical experiments on 11 publically-available real-world datasets demonstrated that the proposed method is effective and promising for deriving communities in complex networks, as well as outperforming state-of-the-art deep learning community detection algorithms.
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Affiliation(s)
- Mohammed Al-Andoli
- Faculty of Information Science and Technology, Multimedia University, Bukit Beruang, Melaka, Malaysia
| | - Wooi Ping Cheah
- Faculty of Information Science and Technology, Multimedia University, Bukit Beruang, Melaka, Malaysia
- School of Computer Science, Faculty of Science and Engineering, University of Nottingham Ningbo China, Ningbo, China
| | - Shing Chiang Tan
- Faculty of Information Science and Technology, Multimedia University, Bukit Beruang, Melaka, Malaysia
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Yang Y, Deng S, Lu J, Li Y, Gong Z, U LH, Hao Z. GraphLSHC: Towards large scale spectral hypergraph clustering. Inf Sci (N Y) 2021. [DOI: 10.1016/j.ins.2020.07.018] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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