Semi-supervised community detection method based on generative adversarial networks

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dc.contributor.authorLiu, X.
dc.contributor.authorZhang, M.
dc.contributor.authorLiu, Y.
dc.contributor.authorLiu, C.
dc.contributor.authorLi, C.
dc.contributor.authorWang, W.
dc.contributor.authorZhang X.
dc.date.accessioned2024-05-19T14:33:40Z
dc.date.available2024-05-19T14:33:40Z
dc.date.issued2024
dc.departmentİstinye Üniversitesien_US
dc.description.abstractCommunity detection in complex networks often suffers from insufficient data and limited utilization of prior knowledge. In this paper we propose “Semi-supervised Generative Adversarial Network” (GANSE), a novel algorithm that integrates Generative Adversarial Networks (GANs) and semi-supervised learning to address these challenges. This method addresses the issues above through a multi-step process. Initially, the network is rewired using vertex similarity metrics, thereby enhancing its structural integrity. Subsequently, a novel generative adversarial network model is designed, and our model facilitates the reconstruction of the network, thereby yielding partitions. Which form the basis for identifying core communities. Additionally, the local clustering coefficient is incorporated as a reward signal and injected into the node selection process. Moreover, isolated nodes are reallocated, ultimately culminating in the derivation of the final community structure. Experimental results on four large real-life datasets demonstrate the clear superiority of the proposed algorithm in terms of F1 and Jaccard metrics when compared to existing algorithms. Notably, our GANSE method outperforms the traditional algorithms in networks with “missing data”. Thus showing its robustness and effectiveness in real-world incomplete datasets. Our findings highlight the potential of GANs and semi-supervised learning for enhancing community detection accuracy in complex networks. © 2024 The Author(s)en_US
dc.description.sponsorship2023NDZD09; Chongqing Municipal Education Commission, CQMEC: 23SKGH247; Chongqing Municipal Education Commission, CQMECen_US
dc.description.sponsorshipThis work is supported in part by Chongqing Federation of Social Sciences Key Project ( 2023NDZD09 ), Humanities and Social Sciences Research Key Project of Chongqing Municipal Education Commission ( 23SKGH247 ).en_US
dc.identifier.doi10.1016/j.jksuci.2024.102008
dc.identifier.issn1319-1578
dc.identifier.issue3en_US
dc.identifier.scopus2-s2.0-85188469472en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://doi.org/10.1016/j.jksuci.2024.102008
dc.identifier.urihttps://hdl.handle.net/20.500.12713/4301
dc.identifier.volume36en_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherKing Saud bin Abdulaziz Universityen_US
dc.relation.ispartofJournal of King Saud University - Computer and Information Sciencesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmz20240519_kaen_US
dc.subjectCommunity Detectionen_US
dc.subjectComplex Networksen_US
dc.subjectGenerative Adversarial Networksen_US
dc.subjectSemi-Unsupervised Learningen_US
dc.titleSemi-supervised community detection method based on generative adversarial networksen_US
dc.typeArticleen_US

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