DLGTrust: Graph neural network-based trust evaluation using dynamic line graph

Trust serves as the foundation for ensuring secure interactions among network entities. However, existing trust evaluation models suffer from vulnerability to global attacks, dependency on multi-layer stacking, and difficulty adapting to dynamically sparse networks. To address these limitations, a graph neural network-based dynamic and robust trust evaluation model is proposed, named DLGTrust. Dynamic line graph snapshots are used to explicitly map indirect trust to direct connections, enhancing the model’s capability to capture complex interactions and its adaptability to sparse data. By integrating a multimodal spatial feature extraction and gated recurrent unit-driven spatiotemporal fusion mechanism, fine-grained modeling of complex interactions is achieved. Simultaneously, adversarial perturbation injection and global robustness constraints are introduced to enhance the model’s defense against global attacks. Experimental results on three real-world datasets show that the comprehensive performance of DLGTrust is improved by at least 26.3% compared to the baseline model. The F1-macro in both observed and unobserved node scenarios is over 98%. Under bad-mouthing, good-mouthing, and global attack rates each set to 10%, the F1-macro is improved by 24.5%, 23.7%, and 76.7%, respectively. The robustness and defense capability of DLGTrust are enhanced. Consequently, DLGTrust offers effective support for secure interactions among entities.