Graph augmentation for node-level few-shot learning

Abstract

In graph few-shot learning, few-shot node classification (FSNC) at the node-level is a popular downstream task. Previous FSNC methods primarily rely on meta-learning or metric learning techniques, aiming to mine prior knowledge from the base classes. However, these methods still have some limitations that need to be addressed, namely: (1) conducting multiple tasks for parameter initialization leads to expensive time costs. (2) ignoring the rich information present in novel classes leads to model over-fitting. To address these issues, this paper proposes a novel graph augmentation method for FSNC on graph data, which includes both parameter initialization and parameter fine-tuning. Specifically, the parameter initialization conducts only one multi-classification task on the base classes, improving generalization ability and reducing time costs. The parameter fine-tuning is designed to include two data augmentation modules (i.e., support augmentation and shot augmentation) on the novel classes to mine the rich information, thus alleviating model over-fitting. As a result, this paper introduces the first graph augmentation method for FSNC. Experimental results showed that our method achieves supreme performance, compared with state-of-the-art FSNC methods.