Few-shot Node Classification (FSNC) is a challenge in graph representation learning where only a few labeled nodes per class are available for training. To tackle this issue, meta-learning has been proposed to transfer structural knowledge from base classes with abundant labels to target novel classes. However, existing solutions become ineffective or inapplicable when base classes have no or limited labeled nodes. To address this challenge, we propose an innovative method dubbed Virtual Node Tuning (VNT). Our approach utilizes a pretrained graph transformer as the encoder and injects virtual nodes as soft prompts in the embedding space, which can be optimized with few-shot labels in novel classes to modulate node embeddings for each specific FSNC task. A unique feature of VNT is that, by incorporating a Graph-based Pseudo Prompt Evolution (GPPE) module, VNT-GPPE can handle scenarios with sparse labels in base classes. Experimental results on four datasets demonstrate the superiority of the proposed approach in addressing FSNC with unlabeled or sparsely labeled base classes, outperforming existing state-of-the-art methods and even fully supervised baselines.

翻译：小样本节点分类（FSNC）是图表示学习中的一个挑战，其中每个类别仅有少量标注节点可用于训练。为解决此问题，元学习被提出用于将有丰富标注的基础类别的结构知识迁移至目标新类别。然而，当基础类别无标注节点或标注节点极其有限时，现有方法会失效或不可用。为应对这一挑战，我们提出一种创新方法——虚拟节点微调（VNT）。该方法利用预训练图变换器作为编码器，并在嵌入空间中注入虚拟节点作为软提示，这些虚拟节点可通过新类别中的小样本标签进行优化，从而为每个特定FSNC任务调制节点嵌入。VNT的独特之处在于，通过集成基于图的伪提示进化（GPPE）模块，VNT-GPPE能够应对基础类别中标签稀疏的场景。四个数据集上的实验结果表明，所提方法在解决基础类别无标签或标签稀疏的FSNC问题上具有优越性，性能超越现有最先进方法甚至全监督基线方法。