השוואת שיטות
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| רשתות גרפיות ניתנות להסבר (Explainable Graph Neural Networks - XAI-GNN)× | טרנספורמר ניתן להסבר× | |
|---|---|---|
| תחום | למידה עמוקה | למידה עמוקה |
| משפחה | Machine learning | Machine learning |
| שנת המקור≠ | 2019 | 2017–2021 |
| הוגה השיטה≠ | Ying, Z. et al. (GNNExplainer); broader XAI-GNN field | Vaswani et al. (Transformer); explainability extensions by Chefer et al. and the broader XAI community |
| סוג≠ | Interpretability framework for graph neural networks | Interpretable deep learning model |
| מקור מכונן≠ | 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. link ↗ | 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. link ↗ |
| כינויים | XAI-GNN, GNN explainability, interpretable GNN, explainable GNN | XAI Transformer, Interpretable Transformer, Transparent Transformer, Explainable Attention Model |
| קשורות≠ | 3 | 4 |
| תקציר≠ | 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. |
| ScholarGateמערך נתונים ↗ |
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