Citation

Al-Andoli, Mohammed and Cheah, Wooi Ping and Tan, Shing Chiang (2021) Deep autoencoder-based community detection in complex networks with particle swarm optimization and continuation algorithms. Journal of Intelligent & Fuzzy Systems, 40 (3). pp. 4517-4533. ISSN 1064-1246

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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.

Item Type:	Article
Uncontrolled Keywords:	Continuation method
Subjects:	Q Science > QA Mathematics > QA299.6-433 Analysis
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	08 May 2021 16:10
Last Modified:	08 May 2021 16:10
URII:	http://shdl.mmu.edu.my/id/eprint/8690

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