We investigate Unsupervised Episode Generation methods to solve Few-Shot Node-Classification (FSNC) task via Meta-learning without labels. Dominant meta-learning methodologies for FSNC were developed under the existence of abundant labeled nodes from diverse base classes for training, which however may not be possible to obtain in the real-world. Although a few studies tried to tackle the label-scarcity problem in graph meta-learning, they still rely on a few labeled nodes, which hinders the full utilization of the information of all nodes in a graph. Despite the effectiveness of graph contrastive learning (GCL) methods in the FSNC task without using the label information, they mainly learn generic node embeddings without consideration of the downstream task to be solved, which may limit its performance in the FSNC task. To this end, we propose a simple yet effective unsupervised episode generation method to benefit from the generalization ability of meta-learning for the FSNC task, while resolving the label-scarcity problem. Our proposed method, called Neighbors as Queries (NaQ), generates training episodes based on pre-calculated node-node similarity. Moreover, NaQ is model-agnostic; hence, it can be used to train any existing supervised graph meta-learning methods in an unsupervised manner, while not sacrificing much of their performance or sometimes even improving them. Extensive experimental results demonstrate the potential of our unsupervised episode generation methods for graph meta-learning towards the FSNC task. Our code is available at: https://github.com/JhngJng/NaQ-PyTorch

翻译：我们研究无监督任务生成方法，旨在通过元学习解决无标签场景下的少样本节点分类任务。主流的少样本节点分类元学习方法依赖于来自多样基类的充足标记节点进行训练，然而现实世界中可能无法获取此类标注数据。尽管已有研究尝试解决图元学习中的标签稀缺问题，但这些方法仍需要少量标记节点，这阻碍了对图中所有节点信息的充分利用。虽然图对比学习方法在无需标签信息的少样本节点分类任务中表现有效，但它们主要学习通用节点嵌入，未考虑待解决的下游任务，这可能限制其在少样本节点分类中的性能。为此，我们提出一种简单有效的无监督任务生成方法，在解决标签稀缺问题的同时，利用元学习的泛化能力提升少样本节点分类性能。所提方法名为"邻居作为查询"（Neighbors as Queries, NaQ），基于预计算的节点间相似度生成训练任务。此外，NaQ具有模型无关性，因此可用于以无监督方式训练任意现有有监督图元学习方法，且不会显著牺牲其性能，有时甚至能提升效果。大量实验结果表明，我们提出的无监督任务生成方法在面向少样本节点分类的图元学习中具有显著潜力。完整代码已开源至：https://github.com/JhngJng/NaQ-PyTorch