Comparar métodos

	Rede Neural de Grafos Explicável	Classificação Explicável Baseada em BERT
Área	Aprendizado profundo	Aprendizado profundo
Família	Machine learning	Machine learning
Ano de origem	2019	2019–2020
Autor original	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)
Tipo	Interpretability framework for graph neural networks	Pre-trained transformer classifier with post-hoc or intrinsic explainability
Fonte seminal	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.
Outros nomes	XAI-GNN, GNN explainability, interpretable GNN, explainable GNN	XAI-BERT, interpretable BERT classifier, BERT with post-hoc explanation, transparent BERT classification
Relacionados	3	6
Resumo	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.