In this paper, we investigate Unsupervised Episode Generation methods to solve Few-Shot Node-Classification (FSNC) problem via Meta-learning without labels. Dominant meta-learning methodologies for FSNC were developed under the existence of abundant labeled nodes for training, which however may not be possible to obtain in the real-world. Although few studies have been proposed to tackle the label-scarcity problem, they still rely on a limited amount of labeled data, which hinders the full utilization of the information of all nodes in a graph. Despite the effectiveness of Self-Supervised Learning (SSL) approaches on FSNC without labels, they mainly learn generic node embeddings without consideration on the downstream task to be solved, which may limit its performance. In this work, we propose unsupervised episode generation methods to benefit from their generalization ability for FSNC tasks while resolving label-scarcity problem. We first propose a method that utilizes graph augmentation to generate training episodes called g-UMTRA, which however has several drawbacks, i.e., 1) increased training time due to the computation of augmented features and 2) low applicability to existing baselines. Hence, we propose Neighbors as Queries (NaQ), which generates episodes from structural neighbors found by graph diffusion. Our proposed methods are model-agnostic, that is, they can be plugged into any existing graph meta-learning models, while not sacrificing much of their performance or sometimes even improving them. We provide theoretical insights to support why our unsupervised episode generation methodologies work, and extensive experimental results demonstrate the potential of our unsupervised episode generation methods for graph meta-learning towards FSNC problems.

翻译：本文探究无监督情节生成方法，旨在通过元学习解决无标签条件下的少样本节点分类问题。目前主流的少样本节点分类元学习方法均基于训练阶段存在大量标签节点这一假设，然而现实场景中此类条件往往难以满足。尽管已有研究尝试解决标签匮乏问题，但这些方法仍需依赖少量标签数据，导致无法充分利用图中所有节点的信息。自监督学习虽能在无标签条件下有效处理少样本节点分类问题，但其主要学习通用节点表征，未考虑下游任务特性，因而性能受限。本文提出无监督情节生成方法，在保留元学习泛化能力的同时解决标签匮乏问题。我们首先提出基于图增强生成训练情节的g-UMTRA方法，但该方法存在两大缺陷：（1）增强特征计算导致训练时间增加；（2）对现有基线方法的适用性较低。因此，我们提出"邻域即查询"（NaQ）方法，通过图扩散获取结构邻居并生成训练情节。所提方法具有模型无关特性，即可灵活嵌入任意现有图元学习模型，在保持原有性能的同时甚至能提升部分模型表现。我们从理论层面论证了无监督情节生成方法的有效性，并通过大量实验证明该方法在处理少样本节点分类问题中的图元学习任务时具有显著潜力。