Spatiotemporal Trust Evaluation for Collaborator Selection via Customized GNN-Mamba

TL;DR

This paper introduces a GNN-Mamba model that combines spatial and temporal trust assessments with resource evaluation to improve collaborator selection in collaborative tasks.

Contribution

It presents a novel customized GNN-Mamba framework that effectively integrates diverse trust perspectives and spatiotemporal dependencies for trust evaluation.

Findings

The GM model outperforms baseline methods in trust accuracy.

It effectively captures short-term and long-term trust dynamics.

Incorporating resource trust improves practical trust assessments.

Abstract

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.