GRExplainer: Universal Explanations for Temporal Graph Neural Networks
Published:Dec 28, 2025 04:24
•1 min read
•ArXiv
Analysis
This paper addresses the critical need for explainability in Temporal Graph Neural Networks (TGNNs), which are increasingly used for dynamic graph analysis. The proposed GRExplainer method tackles limitations of existing explainability methods by offering a universal, efficient, and user-friendly approach. The focus on generality (supporting various TGNN types), efficiency (reducing computational cost), and user-friendliness (automated explanation generation) is a significant contribution to the field. The experimental validation on real-world datasets and comparison against baselines further strengthens the paper's impact.
Key Takeaways
- •Proposes GRExplainer, a novel method for explaining TGNNs.
- •GRExplainer is designed to be universal, efficient, and user-friendly.
- •It addresses limitations of existing explainability methods.
- •Employs node sequences and RNN-based generative models for explanations.
- •Demonstrates superior performance on real-world datasets.
Reference
“GRExplainer extracts node sequences as a unified feature representation, making it independent of specific input formats and thus applicable to both snapshot-based and event-based TGNNs.”