  Trust evaluation assesses trust relationships between entities and
facilitates decision-making. Machine Learning (ML) shows great potential for
trust evaluation owing to its learning capabilities. In recent years, Graph
Neural Networks (GNNs), as a new ML paradigm, have demonstrated superiority in
dealing with graph data. This has motivated researchers to explore their use in
trust evaluation, as trust relationships among entities can be modeled as a
graph. However, current trust evaluation methods that employ GNNs fail to fully
satisfy the dynamic nature of trust, overlook the adverse effects of
trust-related attacks, and cannot provide convincing explanations on evaluation
results. To address these problems, we propose TrustGuard, a GNN-based accurate
trust evaluation model that supports trust dynamicity, is robust against
typical attacks, and provides explanations through visualization. Specifically,
TrustGuard is designed with a layered architecture that contains a snapshot
input layer, a spatial aggregation layer, a temporal aggregation layer, and a
prediction layer. Among them, the spatial aggregation layer adopts a defense
mechanism to robustly aggregate local trust, and the temporal aggregation layer
applies an attention mechanism for effective learning of temporal patterns.
Extensive experiments on two real-world datasets show that TrustGuard
outperforms state-of-the-art GNN-based trust evaluation models with respect to
trust prediction across single-timeslot and multi-timeslot, even in the
presence of attacks. In addition, TrustGuard can explain its evaluation results
by visualizing both spatial and temporal views.
