Trust prediction infers latent user-user trust relations and provides important support for social recommendation, fake-review and manipulation detection, and risk identification. Graph neural networks have become a prominent approach to trust prediction because of their ability to learn network structures and complex trust dependencies. However, existing methods often rely on a unified representation of trust signals and do not disentangle heterogeneous trust evidence into separate evidence channels, failing to exploit the distinct roles that different evidence channels should play during trust modeling. To address this gap, this paper argues that trust evidence should not be treated as an undifferentiated input, but should be decomposed and used as functional control factors over graph propagation. We propose TCHG, a tri-trust conditioned heterogeneous graph learning framework that decomposes trust evidence into three channels and assigns them distinct functional roles in propagation: entity reliability governs message admission, interaction-behavior reliability modulates propagation strength, and contextual trust adjusts the propagation mode through context-conditioned operator selection. Since the three evidence channels evolve at different temporal scales, TCHG maintains independent temporal states with non-uniform decay rates to prevent rapidly changing contextual signals from overwriting slowly accumulated entity reliability. It further predicts trust probability and calibrates the output probability, improving predictive confidence under sparse or conflicting evidence. Extensive experiments on multiple public trust datasets show that TCHG achieves effective and reliable trust prediction compared with representative trust prediction and heterogeneous graph baselines.

翻译：[translated abstract in Chinese] 信任预测能推断潜在的用戶间信任关系，为社会推荐、虚假评论与操纵检测以及风险识别提供重要支持。图神经网络因具备学习网络结构与复杂信任依赖关系的能力，已成为信任预测的主流方法。然而，现有方法通常依赖统一的信任信号表示，未能将异构信任证据分离至独立的证据通道，从而无法充分利用不同证据通道在信任建模中应发挥的独特作用。针对这一不足，本文认为信任证据不应被视为无差别的输入，而应分解并作为图传播的功能性控制因素。我们提出TCHG，一种三信任条件异构图学习框架，将信任证据分解为三个通道，并赋予它们在传播过程中不同的功能角色：实体可靠性控制消息准入，交互行为可靠性调节传播强度，情境信任通过情境条件算子选择调整传播模式。由于三个证据通道以不同的时间尺度演化，TCHG维护了具有非均匀衰减率的独立时间状态，以防止快速变化的情境信号覆盖缓慢积累的实体可靠性。该框架进一步预测信任概率并校准输出概率，从而在证据稀疏或存在冲突时提升预测置信度。在多个公开信任数据集上的大量实验表明，与代表性信任预测方法和异构图基线相比，TCHG实现了有效且可靠的信任预测。