방법 비교

	설명 가능한 그래프 신경망	설명 가능한 트랜스포머
분야	딥러닝	딥러닝
계열	Machine learning	Machine learning
기원 연도	2019	2017–2021
창시자	Ying, Z. et al. (GNNExplainer); broader XAI-GNN field	Vaswani et al. (Transformer); explainability extensions by Chefer et al. and the broader XAI community
유형	Interpretability framework for graph neural networks	Interpretable deep learning model
원전	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.
별칭	XAI-GNN, GNN explainability, interpretable GNN, explainable GNN	XAI Transformer, Interpretable Transformer, Transparent Transformer, Explainable Attention Model
관련	3	4
요약	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.