  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.
