方法对比
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| 可解释图神经网络× | 图神经网络× | |
|---|---|---|
| 领域≠ | 深度学习 | 网络分析 |
| 方法族≠ | Machine learning | Process / pipeline |
| 起源年份≠ | 2019 | 2017–2018 (major variants) |
| 提出者≠ | Ying, Z. et al. (GNNExplainer); broader XAI-GNN field | — |
| 类型≠ | Interpretability framework for graph neural networks | Deep learning on graph-structured data |
| 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. link ↗ | Kipf, T.N. & Welling, M. (2017). Semi-Supervised Classification with Graph Convolutional Networks. International Conference on Learning Representations (ICLR). DOI ↗ |
| 别名≠ | XAI-GNN, GNN explainability, interpretable GNN, explainable GNN | GNN, GCN, GAT, GraphSAGE |
| 相关≠ | 3 | 5 |
| 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. | A Graph Neural Network (GNN) is a deep learning architecture that operates directly on graph-structured data by combining node features with structural information through iterative neighborhood message passing. The three canonical variants — the Graph Convolutional Network (GCN) introduced by Kipf and Welling in 2017, the Graph Attention Network (GAT) introduced by Veličković et al. in 2018, and GraphSAGE — differ in how they aggregate neighbor information: GCN applies a spectral convolution over the full adjacency, GAT weights neighbors by learned attention scores, and GraphSAGE samples and aggregates local neighborhoods inductively, enabling generalization to unseen nodes. |
| ScholarGate数据集 ↗ |
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