Machine learningDeep learning / NLP / CV
基于图神经网络的迁移学习
基于图神经网络（GNN）的迁移学习是指将一个在大规模源图数据集上预训练的GNN模型，应用于一个规模较小、通常标签稀缺的目标图任务。通过重用学习到的节点和边的表示，该方法在收集足够的带标签图数据成本高昂或耗时（如化学、生物学和社交网络分析中常见的情况）时，能实现强大的预测性能。
阅读完整方法
仅限会员
登录使用免费账户登录即可阅读本节。
Method map
The neighbourhood of related methods — select a node to explore.
来源
- Hu, W., Liu, B., Gomes, J., Zitnik, M., Liang, P., Pande, V., & Leskovec, J. (2020). Strategies for Pre-training Graph Neural Networks. In International Conference on Learning Representations (ICLR 2020). link ↗
- Pan, S. J., & Yang, Q. (2010). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359. DOI: 10.1109/TKDE.2009.191 ↗
如何引用本页
ScholarGate. (2026, June 3). Transfer Learning with Graph Neural Network (Pre-trained GNN Fine-tuning). ScholarGate. https://scholargate.app/zh/deep-learning/transfer-learning-with-graph-neural-network
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
Compare side by side →