DGTEN: A Robust Deep Gaussian based Graph Neural Network for Dynamic Trust Evaluation with Uncertainty-Quantification Support

• URL: http://arxiv.org/abs/2510.07620v1
• Date: Wed, 08 Oct 2025 23:38:55 GMT
• Title: DGTEN: A Robust Deep Gaussian based Graph Neural Network for Dynamic Trust Evaluation with Uncertainty-Quantification Support
• Authors: Muhammad Usman, Yugyung Lee,
• Abstract summary: DGTEN (Deep Gaussian-based Trust Evaluation Network) introduces a unified graph framework.<n>It combines uncertainty-aware message passing, expressive temporal modeling, and built-in defenses against trust-targeted attacks.<n>On two signed Bitcoin trust networks, DGTEN delivers significant improvements.
• Score: 2.4897847232811716
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Dynamic trust evaluation in large, rapidly evolving graphs requires models that can capture changing relationships, express calibrated confidence, and resist adversarial manipulation. DGTEN (Deep Gaussian-based Trust Evaluation Network) introduces a unified graph framework that achieves all three by combining uncertainty-aware message passing, expressive temporal modeling, and built-in defenses against trust-targeted attacks. It represents nodes and edges as Gaussian distributions so that both semantic signals and epistemic uncertainty propagate through the graph neural network, enabling risk-aware trust decisions rather than overconfident guesses. To model how trust evolves, it employs hybrid Absolute-Gaussian-Hourglass (HAGH) positional encoding with Kolmogorov-Arnold network-based unbiased multi-head attention, followed by an ordinary differential equation (ODE)-based residual learning module to jointly capture abrupt shifts and smooth trends. Robust adaptive ensemble coefficient analysis prunes or down-weights suspicious interactions using complementary cosine and Jaccard similarity measures, mitigating reputation laundering, sabotage, and on/off attacks. On two signed Bitcoin trust networks, DGTEN delivers significant improvements: in single-timeslot prediction on Bitcoin-Alpha, it improves MCC by 10.77% over the best dynamic baseline; in the cold-start scenario, it achieves a 16.41% MCC gain - the largest across all tasks and datasets. Under adversarial on/off attacks, it surpasses the baseline by up to 11.63% MCC. These results validate the effectiveness of the unified DGTEN framework.

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