ST-TGExplainer: Disentangling Stability and Transition Patterns for Temporal GNN Interpretability
Researchers have introduced ST-TGExplainer, a novel method designed to improve the interpretability of Temporal Graph Neural Networks (TGNNs). Existing models often struggle to distinguish between the influence of past interactions (stability patterns) and newly emerging ones (transition patterns) on predictions. ST-TGExplainer addresses this by learning a compact explanatory subgraph that disentangles these two pattern types, ensuring predictive accuracy while reducing redundancy. Experiments show that this approach leads to more faithful explanations for TGNN predictions. AI
IMPACT Enhances the explainability of temporal graph models, potentially improving trust and adoption in applications relying on dynamic graph data.