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

2025-12-12

AI Summary

To address the lack of context awareness, temporal modeling, and heterogeneous semantic integration in trust prediction for dynamic heterogeneous networks, this paper proposes CT-GNN—the first unified framework for this task. Methodologically, it introduces (1) a context-aware continuous-time graph neural network enabling fine-grained trust inference; (2) a two-level heterogeneous attention mechanism—comprising cross-type and intra-type attention—to jointly model node heterogeneity and temporal dynamics; and (3) a meta-path-driven context feature extractor coupled with a context-aware aggregator. Extensive experiments on three real-world datasets demonstrate that CT-GNN significantly outperforms five categories of state-of-the-art baselines. Moreover, it exhibits strong scalability to large-scale graphs and robustness against both trust-specific and GNN-targeted adversarial attacks.

Technology Category

• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
• Machine Learning: Graph-based Machine Learning
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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.

Problem

• Predict trust in dynamic heterogeneous networks with context-awareness
• Overcome limitations of current GNN models in capturing trust dynamicity
• Address lack of context-awareness and heterogeneous semantics in trust prediction

Innovation

• Context-aware GNN model for trust prediction
• Dual attention mechanism for heterogeneous networks
• Continuous-time representation for dynamic graphs