  Although Graph Neural Networks (GNNs) have been successful in node
classification tasks, their performance heavily relies on the availability of a
sufficient number of labeled nodes per class. In real-world situations, not all
classes have many labeled nodes and there may be instances where the model
needs to classify new classes, making manual labeling difficult. To solve this
problem, it is important for GNNs to be able to classify nodes with a limited
number of labeled nodes, known as few-shot node classification. Previous
episodic meta-learning based methods have demonstrated success in few-shot node
classification, but our findings suggest that optimal performance can only be
achieved with a substantial amount of diverse training meta-tasks. To address
this challenge of meta-learning based few-shot learning (FSL), we propose a new
approach, the Task-Equivariant Graph few-shot learning (TEG) framework. Our TEG
framework enables the model to learn transferable task-adaptation strategies
using a limited number of training meta-tasks, allowing it to acquire
meta-knowledge for a wide range of meta-tasks. By incorporating equivariant
neural networks, TEG can utilize their strong generalization abilities to learn
highly adaptable task-specific strategies. As a result, TEG achieves
state-of-the-art performance with limited training meta-tasks. Our experiments
on various benchmark datasets demonstrate TEG's superiority in terms of
accuracy and generalization ability, even when using minimal meta-training
data, highlighting the effectiveness of our proposed approach in addressing the
challenges of meta-learning based few-shot node classification. Our code is
available at the following link: https://github.com/sung-won-kim/TEG
