Few-shot node classification aims at classifying nodes with limited labeled
nodes as references.
Recent few-shot node classification methods typically learn from classes with
abundant labeled nodes (i.e., meta-training classes) and then generalize to
classes with limited labeled nodes (i.e., meta-test classes). Nevertheless, on
real-world graphs, it is usually difficult to obtain abundant labeled nodes for
many classes. In practice, each meta-training class can only consist of several
labeled nodes, known as the extremely weak supervision problem. In few-shot
node classification, with extremely limited labeled nodes for meta-training,
the generalization gap between meta-training and meta-test will become larger
and thus lead to suboptimal performance. To tackle this issue, we study a novel
problem of few-shot node classification with extremely weak supervision and
propose a principled framework X-FNC under the prevalent meta-learning
framework. Specifically, our goal is to accumulate meta-knowledge across
different meta-training tasks with extremely weak supervision and generalize
such knowledge to meta-test tasks. To address the challenges resulting from
extremely scarce labeled nodes, we propose two essential modules to obtain
pseudo-labeled nodes as extra references and effectively learn from extremely
limited supervision information. We further conduct extensive experiments on
four node classification datasets with extremely weak supervision to validate
the superiority of our framework compared to the state-of-the-art baselines.

该研究针对极度缺乏标记节点的 few-shot node classification 问题，提出了一个基于元学习框架的 X-FNC 模型，使用伪标记节点和元知识积累解决了极度弱监督信息下的分类问题。