{"ID":2873194,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.12240","arxiv_id":"2509.12240","title":"Accurate Trust Evaluation for Effective Operation of Social IoT Systems via Hypergraph-Enabled Self-Supervised Contrastive Learning","abstract":"Social Internet-of-Things (IoT) enhances collaboration between devices by endowing IoT systems with social attributes. However, calculating trust between devices based on complex and dynamic social attributes-similar to trust formation mechanisms in human society-poses a significant challenge. To address this issue, this paper presents a new hypergraph-enabled self-supervised contrastive learning (HSCL) method to accurately determine trust values between devices. To implement the proposed HSCL, hypergraphs are first used to discover and represent high-order relationships based on social attributes. Hypergraph augmentation is then applied to enhance the semantics of the generated social hypergraph, followed by the use of a parameter-sharing hypergraph neural network to nonlinearly fuse the high-order social relationships. Additionally, a self-supervised contrastive learning method is utilized to obtain meaningful device embeddings by conducting comparisons among devices, hyperedges, and device-to-hyperedge relationships. Finally, trust values between devices are calculated based on device embeddings that encapsulate high-order social relationships. Extensive experiments reveal that the proposed HSCL method outperforms baseline algorithms in effectively distinguishing between trusted and untrusted nodes and identifying the most trusted node.","short_abstract":"Social Internet-of-Things (IoT) enhances collaboration between devices by endowing IoT systems with social attributes. However, calculating trust between devices based on complex and dynamic social attributes-similar to trust formation mechanisms in human society-poses a significant challenge. To address this issue, th...","url_abs":"https://arxiv.org/abs/2509.12240","url_pdf":"https://arxiv.org/pdf/2509.12240v1","authors":"[\"Botao Zhu\",\"Xianbin Wang\"]","published":"2025-09-09T21:06:13Z","proceeding":"cs.SI","tasks":"[\"cs.SI\"]","methods":"[\"Graph Neural Network\"]","has_code":false}