Deep autoencoder-based community detection in complex networks with particle swarm optimization and continuation algorithms

Mohammed Al-Andoli, Wooi Ping Cheah, Shing Chiang Tan

Research output: Journal PublicationArticlepeer-review

7 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)4517-4533
Number of pages17
JournalJournal of Intelligent and Fuzzy Systems
Volume40
Issue number3
DOIs
Publication statusPublished - 2021

Keywords

  • autoencoder
  • community detection
  • Complex networks
  • continuation method
  • particle swarm optimization

ASJC Scopus subject areas

  • Statistics and Probability
  • Engineering (all)
  • Artificial Intelligence

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