Spatiotemporal Trust Evaluation for Collaborator Selection via Customized GNN-Mamba
For collaborative task systems, this work addresses the challenge of accurate trust evaluation under spatiotemporal dependencies, but the improvement is incremental over existing methods.
The paper proposes a GNN-Mamba model for trust evaluation in collaborator selection, integrating spatial dependencies from historical collaborations and temporal dynamics of device trust. The model outperforms baselines in accuracy and stability of trust evaluation.
The successful completion of collaborative tasks relies on the effective selection of trustworthy collaborators. To accurately evaluate the trustworthiness of potential collaborators, it is necessary to combine insights from their past collaborations with assessments of their resource capabilities under specific task contexts. However, the coexistence of diverse trust perspectives, along with complex spatiotemporal dependencies among devices, makes accurate trust evaluation particularly challenging. To address these challenges, we propose a customized Graph Neural Network (GNN)-Mamba (GM) model for trust evaluation and collaborator selection. In this model, the GNN model performs spatial trust fusion by leveraging inter-device spatial dependencies extracted from historical collaborations, while the Mamba-based temporal model captures both short-term fluctuations and long-term evolution of device trust. In addition, task-specific resource trust is incorporated to reflect the practical capabilities of devices under varying task conditions. Experimental results demonstrate that the proposed GM model outperforms baseline approaches in terms of the accuracy and stability of trust evaluation.