First-Order Meta-Learning in Node Classification with Graph Convolutional Network

Abstract

In recent years, research on graph representation learning within graph neural networks has made great progress. Among them, graph convolutional neural networks (GCN), which is based on spectral domain, have drawn great attention and become one of the mainstream graph embedding approaches. However, few-shot learning on graphs has little related work, as we know. Toward this problem, we propose a meta-learning method on graphs, FOML-GCN (First order meta-learning on GCN), aimed to be applied in various kinds of models of graph convolutional networks. As many common approaches in meta-learning, FOML-GCN involves two phases, meta-training and meta-testing. By learning the potential Knowledge from a small number of samples in existing classes during meta-training, our method enhances the ability in node classification of the base model by training within new few data that only appear in the meta-testing step. The experiment on two benchmark datasets shows that our FOML-GCN performs better than base GCN and other models of graph network in few-shot node classification tasks.