Machine learningDeep learning / NLP / CV
可解释图神经网络
可解释图神经网络（Explainable Graph Neural Networks, XAI-GNN）将标准的图神经网络（GNN）架构与事后解释或内在解释技术相结合，以揭示哪些节点、边和节点特征驱动了模型的预测。该领域由 GNNExplainer（Ying et al., 2019）开创，旨在解决 GNN 的黑箱问题，对于任何需要信任或审计的基于图的预测都至关重要。
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来源
- 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 ↗
- Yuan, H., Yu, H., Gui, S., & Ji, S. (2023). Explainability in Graph Neural Networks: A Taxonomic Survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(5), 5782–5799. DOI: 10.1109/TPAMI.2022.3204236 ↗
如何引用本页
ScholarGate. (2026, June 3). Explainable Graph Neural Network (XAI-GNN). ScholarGate. https://scholargate.app/zh/deep-learning/explainable-graph-neural-network
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