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.
@article{arxiv.1910.03053,
title = {Graph Few-shot Learning via Knowledge Transfer},
author = {Huaxiu Yao and Chuxu Zhang and Ying Wei and Meng Jiang and Suhang Wang and Junzhou Huang and Nitesh V. Chawla and Zhenhui Li},
journal= {arXiv preprint arXiv:1910.03053},
year = {2020}
}