Few-shot Edge Classification in Graph Meta-learning

Recent few-shot learning methods based on graph neural networks (GNN) over-focus on the connections between nodes while ignoring the pair-wise relations between nodes. Metalearning aims at model parameter initialization, enabling the model to gain the generalization capability, which assists GNN to pay more attention to nodes. There are rare methods to apply meta-learning to non-Euclidean spaces (such as graph structures). Thus, we propose a graph meta-learning framework, Meta Edgelabeling Graph Neural Network (Meta-EGNN), to solve image classification in few-shot learning. Meta-EGNN can learn a better parameter initialization for GNN with the prediction of edge labels, which can enhance the generalization of the model on unseen tasks. We also introduce the first-order gradient model-agnostic meta-learning into meta-EGNN, which can not only reduce the computational costs, but also help meta-EGNN extend to the transductive inference. The experimental results on two benchmarks prove that Meta-EGNN is competitive in both supervised and semi-supervised image classification.