So sánh phương pháp

	Mạng nơ-ron đồ thị có thể giải thích (Explainable Graph Neural Network)	Explainable Transformer (Transformer có thể giải thích được)
Lĩnh vực	Học sâu	Học sâu
Họ	Machine learning	Machine learning
Năm ra đời	2019	2017–2021
Người khởi xướng	Ying, Z. et al. (GNNExplainer); broader XAI-GNN field	Vaswani et al. (Transformer); explainability extensions by Chefer et al. and the broader XAI community
Loại	Interpretability framework for graph neural networks	Interpretable deep learning model
Công trình gốc	Ying, Z., Bourgeois, D., You, J., Zitnik, M., & Leskovec, J. (2019). GNNExplainer: Generating Explanations for Graph Neural Networks. Advances in Neural Information Processing Systems (NeurIPS), 32, 9240–9251.	Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30.
Tên gọi khác	XAI-GNN, GNN explainability, interpretable GNN, explainable GNN	XAI Transformer, Interpretable Transformer, Transparent Transformer, Explainable Attention Model
Liên quan	3	4
Tóm tắt	Explainable Graph Neural Networks (XAI-GNN) combine standard GNN architectures with post-hoc or intrinsic explanation techniques that reveal which nodes, edges, and node features drove a model's prediction. Pioneered by GNNExplainer (Ying et al., 2019), the field addresses the black-box critique of GNNs and is essential wherever graph-based predictions must be trusted or audited.	An Explainable Transformer combines a standard or pre-trained Transformer architecture with post-hoc or built-in interpretability techniques — such as attention rollout, gradient-weighted attention, or SHAP — to reveal which input tokens or regions drove each prediction. The approach bridges high predictive accuracy with the transparency required in high-stakes or regulated domains.