TCHG: Tri-Trust Conditioned Heterogeneous Graph Learning for Reliable Dynamic Trust Prediction
作者: Bohao Liao, Boyu Deng, Qipeng Song, Jieling Wang, Jingchao Wang
分类: cs.LG
发布日期: 2026-06-15
备注: 18 pages, 10 figures, 13 tables
💡 一句话要点
提出TCHG框架以解决动态信任预测中的异构信任证据问题
🎯 匹配领域: 支柱一：机器人控制 (Robot Control)
关键词: 信任预测 图神经网络 异构图学习 动态信任 社交推荐 风险识别 数据挖掘
📋 核心要点
- 现有信任预测方法未能有效分解异构信任证据，导致信任建模的局限性。
- TCHG框架通过将信任证据分解为三个通道，分别控制消息接纳、传播强度和传播模式。
- 在多个公共信任数据集上的实验表明，TCHG在信任预测上优于现有基线，提升了预测的可靠性。
📝 摘要（中文）
信任预测推断潜在的用户间信任关系，为社交推荐、假评论检测和风险识别提供重要支持。图神经网络因其学习网络结构和复杂信任依赖关系的能力而成为信任预测的主要方法。然而，现有方法通常依赖于统一的信任信号表示，未能将异构信任证据分解为独立的证据通道，未能充分利用不同证据通道在信任建模中的不同角色。为此，本文提出TCHG框架，将信任证据分解为三个通道，并在传播中赋予它们不同的功能角色，从而提高信任预测的有效性和可靠性。
🔬 方法详解
问题定义：本文旨在解决现有信任预测方法对异构信任证据的统一处理问题，导致信任建模效果不佳。
核心思路：提出TCHG框架，将信任证据分解为三个独立通道，分别控制消息接纳、传播强度和传播模式，以更好地利用不同证据的特性。
技术框架：TCHG框架包括三个主要模块：实体可靠性通道、交互行为可靠性通道和上下文信任通道，分别负责不同的传播功能，并维护独立的时间状态。
关键创新：最重要的创新在于将信任证据分解为三个功能性通道，解决了现有方法未能区分不同信任证据角色的问题，显著提升了信任预测的准确性。
关键设计：在设计中，TCHG采用非均匀衰减率来维护不同时间尺度的证据通道，并在输出概率的校准上进行了优化，以提高在稀疏或冲突证据下的预测信心。
🖼️ 关键图片
📊 实验亮点
在多个公共信任数据集上的实验结果显示，TCHG在信任预测任务中显著优于现有的信任预测和异构图基线，具体提升幅度达到XX%，有效提高了预测的可靠性和准确性。
🎯 应用场景
该研究具有广泛的应用潜力，尤其在社交网络、在线评论系统和风险管理等领域。通过提高信任预测的准确性，TCHG可以帮助平台更好地识别虚假信息和用户行为，从而提升用户体验和平台安全性。
📄 摘要（原文）
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.