2023
DOI: 10.1016/j.engappai.2022.105657
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Community detection in networks through a deep robust auto-encoder nonnegative matrix factorization

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Cited by 12 publications
(2 citation statements)
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“…Bio-inspired optimization has successfully solved the problem of community detection in network that we observe from the work Osaba et al ( 2020 ) where the authors present in detail the problem of community detection from view of bio-inspired computation. Works like Al-sharoa and Rahahleh ( 2023 ) show a deep robust auto-encoder nonnegative matrix factorization (DRANMF) approach consisting of a deep structured decoder and encoder components to detect the community structure in networks.…”
Section: Related Workmentioning
confidence: 99%
“…Bio-inspired optimization has successfully solved the problem of community detection in network that we observe from the work Osaba et al ( 2020 ) where the authors present in detail the problem of community detection from view of bio-inspired computation. Works like Al-sharoa and Rahahleh ( 2023 ) show a deep robust auto-encoder nonnegative matrix factorization (DRANMF) approach consisting of a deep structured decoder and encoder components to detect the community structure in networks.…”
Section: Related Workmentioning
confidence: 99%
“…The deep autoencoder, an extensively employed unsupervised learning algorithm in the field of deep learning [19], is capable of performing various tasks such as feature extraction, dimensionality reduction, and data reconstruction. In addition, it can also bridge the gap between low-level to highlevel networks for optimal community detection [20]. The deep autoencoder consists of two components: an encoder component and a decoder component [21].…”
Section: Introductionmentioning
confidence: 99%