Deep autoencoder-based community detection in complex networks with particle swarm optimization and continuation algorithms
| Autor: | Shing Chiang Tan, Mohammed Al-Andoli, Wooi Ping Cheah |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Statistics and Probability
Modularity (networks) Computer science business.industry Deep learning General Engineering Particle swarm optimization 02 engineering and technology Complex network Autoencoder Backpropagation Artificial Intelligence 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence Gradient descent business Algorithm Premature convergence |
| Zdroj: | Journal of Intelligent & Fuzzy Systems. 40:4517-4533 |
| ISSN: | 1875-8967 1064-1246 |
| DOI: | 10.3233/jifs-201342 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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