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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.<\/jats:p>","DOI":"10.3233\/jifs-201342","type":"journal-article","created":{"date-parts":[[2021,1,29]],"date-time":"2021-01-29T12:13:28Z","timestamp":1611922408000},"page":"4517-4533","source":"Crossref","is-referenced-by-count":13,"title":["Deep autoencoder-based community detection in complex networks with particle swarm optimization and continuation algorithms"],"prefix":"10.1177","volume":"40","author":[{"given":"Mohammed","family":"Al-Andoli","sequence":"first","affiliation":[{"name":"Faculty of Information Science and Technology, Multimedia University, Bukit Beruang, Melaka, Malaysia"}]},{"given":"Wooi Ping","family":"Cheah","sequence":"additional","affiliation":[{"name":"Faculty of Information Science and 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