Trust 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 attacks on trust evaluation, 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.

翻译：信任评估用于评估实体间的信任关系并辅助决策。机器学习因其学习能力在信任评估中展现出巨大潜力。近年来，图神经网络作为一种新兴的机器学习范式，在处理图数据方面表现出优越性。这促使研究者探索其在信任评估中的应用，因为实体间的信任关系可建模为图结构。然而，当前基于图神经网络的信任评估方法未能充分满足信任的动态性，忽视攻击对信任评估的负面影响，且无法对评估结果提供令人信服的解释。针对这些问题，我们提出TrustGuard——一种基于图神经网络的精确信任评估模型，该模型支持信任动态性，对典型攻击具有鲁棒性，并通过可视化提供解释。具体而言，TrustGuard采用分层架构设计，包含快照输入层、空间聚合层、时间聚合层和预测层。其中，空间聚合层采用防御机制实现局部信任的鲁棒聚合，时间聚合层应用注意力机制有效学习时序模式。在两个真实数据集上的大量实验表明，TrustGuard在单时间槽和多时间槽的信任预测中均优于现有基于图神经网络的信任评估模型，即便在存在攻击的情况下仍保持优势。此外，TrustGuard可通过空间和时间视图的可视化解释其评估结果。