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Transfer Learning with Graph Neural Network

Transfer Learning with Graph Neural Networks (GNNs) adapts a GNN pre-trained on a large source graph dataset to a smaller, often label-scarce target graph task. By reusing learned node and edge representations, this approach achieves strong predictive performance where collecting sufficient labeled graph data is expensive or slow — as is common in chemistry, biology, and social network analysis.

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Sources

  1. 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 ↗
  2. 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 ↗

Related methods

Graph Neural Network (Network Analysis)Transfer Learning with BERT-based ClassificationTransfer Learning with Convolutional Neural Network

Referenced by

Multilingual graph neural network

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ScholarGate — Transfer Learning with Graph Neural Network (Transfer Learning with Graph Neural Network (Pre-trained GNN Fine-tuning)). Retrieved 2026-06-04 from https://scholargate.app/en/deep-learning/transfer-learning-with-graph-neural-network