TrustGuard: GNN-based robust and explainable trust evaluation with dynamicity support

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dc.authorscopusidWitold Pedrycz / 58861905800
dc.authorwosidWitold Pedrycz / HJZ-2779-2023
dc.contributor.authorWang, Jie
dc.contributor.authorYan, Zheng
dc.contributor.authorLan, Jiahe
dc.contributor.authorBertino, Elisa
dc.contributor.authorPedrycz, Witold
dc.date.accessioned2025-04-16T13:44:30Z
dc.date.available2025-04-16T13:44:30Z
dc.date.issued2024
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractTrust evaluation assesses trust relationships between entities and facilitates decision-making. Machine Learning (ML) shows great potential for trust evaluation owing to its learning capabilities. In recent years, Graph Neural Networks (GNNs), as a new ML paradigm, have demonstrated superiority in dealing with graph data. This has motivated researchers to explore their use in trust evaluation, as trust relationships among entities can be modeled as a graph. However, current trust evaluation methods that employ GNNs fail to fully satisfy the dynamic nature of trust, overlook the adverse effects of trust-related attacks, and cannot provide convincing explanations on evaluation results. To address these problems, we propose TrustGuard, a GNN-based accurate trust evaluation model that supports trust dynamicity, is robust against typical attacks, and provides explanations through visualization. Specifically, TrustGuard is designed with a layered architecture that contains a snapshot input layer, a spatial aggregation layer, a temporal aggregation layer, and a prediction layer. Among them, the spatial aggregation layer adopts a defense mechanism to robustly aggregate local trust, and the temporal aggregation layer applies an attention mechanism for effective learning of temporal patterns. Extensive experiments on two real-world datasets show that TrustGuard outperforms state-of-the-art GNN-based trust evaluation models with respect to trust prediction across single-timeslot and multi-timeslot, even in the presence of attacks. In addition, TrustGuard can explain its evaluation results by visualizing both spatial and temporal views.
dc.description.sponsorshipNational Natural Science Foundation of China Key Research Project of Shaanxi Natural Science Foundation Higher Education Discipline Innovation Project
dc.identifier.citationWang, J., Yan, Z., Lan, J., Bertino, E., & Pedrycz, W. (2024). TrustGuard: GNN-based robust and explainable trust evaluation with dynamicity support. IEEE Transactions on Dependable and Secure Computing, 21(5), 4433-4450.
dc.identifier.doi10.1109/TDSC.2024.3353548
dc.identifier.endpage4450
dc.identifier.issn1545-5971
dc.identifier.issn1941-0018
dc.identifier.issue5
dc.identifier.scopus2-s2.0-85182944188
dc.identifier.scopusqualityQ1
dc.identifier.startpage4433
dc.identifier.urihttp://dx.doi.org/10.1109/TDSC.2024.3353548
dc.identifier.urihttps://hdl.handle.net/20.500.12713/6035
dc.identifier.volume21
dc.identifier.wosWOS:001306799100010
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorPedrycz, Witold
dc.institutionauthoridWitold Pedrycz / 0000-0002-9335-9930
dc.language.isoen
dc.publisherInstitute of electrical and electronics engineers inc.
dc.relation.ispartofIEEE transactions on dependable and secure computing
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectDynamicity
dc.subjectExplainability
dc.subjectGraph Neural Network (GNN)
dc.subjectRobustness
dc.subjectTrust Evaluation
dc.titleTrustGuard: GNN-based robust and explainable trust evaluation with dynamicity support
dc.typeArticle

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