Graph Few-Shot Learning via Knowledge Transfer


  • Huaxiu Yao Pennsylvania State University
  • Chuxu Zhang University of Notre Dame
  • Ying Wei Tencent AI Lab
  • Meng Jiang University of Notre Dame
  • Suhang Wang Pennsylvania State University
  • Junzhou Huang Tencent AI Lab
  • Nitesh Chawla University of Notre Dame
  • Zhenhui Li Pennsylvania State University



Towards the challenging problem of semi-supervised node classification, there have been extensive studies. As a frontier, Graph Neural Networks (GNNs) have aroused great interest recently, which update the representation of each node by aggregating information of its neighbors. However, most GNNs have shallow layers with a limited receptive field and may not achieve satisfactory performance especially when the number of labeled nodes is quite small. To address this challenge, we innovatively propose a graph few-shot learning (GFL) algorithm that incorporates prior knowledge learned from auxiliary graphs to improve classification accuracy on the target graph. Specifically, a transferable metric space characterized by a node embedding and a graph-specific prototype embedding function is shared between auxiliary graphs and the target, facilitating the transfer of structural knowledge. Extensive experiments and ablation studies on four real-world graph datasets demonstrate the effectiveness of our proposed model and the contribution of each component.




How to Cite

Yao, H., Zhang, C., Wei, Y., Jiang, M., Wang, S., Huang, J., Chawla, N., & Li, Z. (2020). Graph Few-Shot Learning via Knowledge Transfer. Proceedings of the AAAI Conference on Artificial Intelligence, 34(04), 6656-6663.



AAAI Technical Track: Machine Learning