TCHG: Tri-Trust Conditioned Heterogeneous Graph Learning for Reliable Dynamic Trust Prediction
arXiv:2606.16611v1 Announce Type: new Abstract: 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 di
The proliferation of AI-driven social systems and the increasing sophistication of online manipulation necessitate more robust methods for trust prediction.
Improved trust prediction models are crucial for maintaining the integrity of online platforms, detecting fraud, and enhancing the reliability of AI-driven recommendations and social interactions.
The ability to disentangle heterogeneous trust signals could significantly improve the accuracy and resilience of systems designed to identify fake reviews, detect manipulation, and prevent risk.
- · Social media platforms
- · E-commerce
- · Cybersecurity firms
- · AI ethics research
- · Malicious actors
- · Disinformation networks
- · Fake review services
More accurate identification of fraudulent online activities and manipulation attempts.
Increased user confidence in online platforms and recommendations, potentially leading to higher engagement and economic activity.
The development of more secure and trustworthy AI agents that operate within clearer parameters of established trust.
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