方法对比

	可解释图神经网络	可解释的BERT分类
领域	深度学习	深度学习
方法族	Machine learning	Machine learning
起源年份	2019	2019–2020
提出者	Ying, Z. et al. (GNNExplainer); broader XAI-GNN field	Devlin et al. (BERT); explainability methods by Lundberg & Lee (SHAP), Ribeiro et al. (LIME), Sundararajan et al. (Integrated Gradients)
类型	Interpretability framework for graph neural networks	Pre-trained transformer classifier with post-hoc or intrinsic explainability
开创性文献	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.	Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. Proceedings of NAACL-HLT 2019, pp. 4171–4186.
别名	XAI-GNN, GNN explainability, interpretable GNN, explainable GNN	XAI-BERT, interpretable BERT classifier, BERT with post-hoc explanation, transparent BERT classification
相关	3	6
摘要	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.	Explainable BERT-based Classification combines the predictive power of fine-tuned BERT transformers for text classification with post-hoc or intrinsic explainability techniques — such as SHAP, LIME, attention analysis, or integrated gradients — to reveal which words or tokens drove each prediction. The result is a classifier that is both accurate and interpretable enough for high-stakes or auditable NLP applications.