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

翻译：尽管图神经网络（GNN）在节点分类任务中取得了成功，但其性能严重依赖于每个类别有足够数量的标注节点。在现实场景中，并非所有类别都有大量标注节点，且模型可能需要对新类别进行分类，这使得人工标注变得困难。为解决这一问题，GNN需具备在标注节点有限的情况下对节点进行分类的能力，即小样本节点分类。此前基于情节元学习的方法在小样本节点分类中取得了成功，但我们的研究发现，只有通过大量多样化的训练元任务才能实现最优性能。为应对基于元学习的小样本学习（FSL）这一挑战，我们提出了一种新方法——任务等变图小样本学习（TEG）框架。我们的TEG框架使模型能够利用有限的训练元任务学习可迁移的任务适应策略，从而为广泛的元任务获取元知识。通过整合等变神经网络，TEG可利用其强大的泛化能力学习高度可适应的任务特定策略。因此，TEG在有限的训练元任务下实现了最先进的性能。我们在多个基准数据集上的实验表明，即使使用极少的元训练数据，TEG在准确性和泛化能力方面仍具有优越性，这凸显了我们所提方法在应对基于元学习的小样本节点分类挑战中的有效性。我们的代码可在以下链接获取：https://github.com/sung-won-kim/TEG