Title: Accurate Trust Evaluation for Effective Operation of Social IoT Systems via Hypergraph-Enabled Self-Supervised Contrastive Learning Show Chinese title

Title: 通过超图增强的自监督对比学习实现社交物联网系统的准确信任评估

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. Show Chinese abstract

Abstract: 社交物联网（IoT）通过赋予物联网系统社会属性，增强了设备之间的协作。 然而，基于复杂且动态的社会属性计算设备之间的信任——类似于人类社会中的信任形成机制——是一个重大挑战。 为了解决这个问题，本文提出了一种新的超图增强的自监督对比学习（HSCL）方法，以准确确定设备之间的信任值。 为了实现所提出的HSCL，首先使用超图基于社会属性发现和表示高阶关系。 然后应用超图增强来增强生成的社会超图的语义，接着使用参数共享的超图神经网络对高阶社会关系进行非线性融合。 此外，利用一种自监督对比学习方法，通过设备、超边以及设备到超边的关系之间的比较，获得有意义的设备嵌入。 最后，基于包含高阶社会关系的设备嵌入计算设备之间的信任值。 大量实验表明，所提出的HSCL方法在有效区分可信和不可信节点以及识别最可信节点方面优于基线算法。