CAT: Can Trust be Predicted with Context-Awareness in Dynamic Heterogeneous Networks?

• URL: http://arxiv.org/abs/2512.11352v1
• Date: Fri, 12 Dec 2025 08:00:28 GMT
• Title: CAT: Can Trust be Predicted with Context-Awareness in Dynamic Heterogeneous Networks?
• Authors: Jie Wang, Zheng Yan, Jiahe Lan, Xuyan Li, Elisa Bertino,
• Abstract summary: Graph Neural Networks (GNNs) have emerged as a promising approach for trust prediction.<n>These models fail to capture trust dynamicity, leading to questionable inferences.<n>We propose CAT, the first Context-Aware GNN-based Trust prediction model.
• Score: 10.476593803368358
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Trust prediction provides valuable support for decision-making, risk mitigation, and system security enhancement. Recently, Graph Neural Networks (GNNs) have emerged as a promising approach for trust prediction, owing to their ability to learn expressive node representations that capture intricate trust relationships within a network. However, current GNN-based trust prediction models face several limitations: (i) Most of them fail to capture trust dynamicity, leading to questionable inferences. (ii) They rarely consider the heterogeneous nature of real-world networks, resulting in a loss of rich semantics. (iii) None of them support context-awareness, a basic property of trust, making prediction results coarse-grained. To this end, we propose CAT, the first Context-Aware GNN-based Trust prediction model that supports trust dynamicity and accurately represents real-world heterogeneity. CAT consists of a graph construction layer, an embedding layer, a heterogeneous attention layer, and a prediction layer. It handles dynamic graphs using continuous-time representations and captures temporal information through a time encoding function. To model graph heterogeneity and leverage semantic information, CAT employs a dual attention mechanism that identifies the importance of different node types and nodes within each type. For context-awareness, we introduce a new notion of meta-paths to extract contextual features. By constructing context embeddings and integrating a context-aware aggregator, CAT can predict both context-aware trust and overall trust. Extensive experiments on three real-world datasets demonstrate that CAT outperforms five groups of baselines in trust prediction, while exhibiting strong scalability to large-scale graphs and robustness against both trust-oriented and GNN-oriented attacks.

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