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.

本研究提出一种名为Virtual Node Tuning (VNT)的创新方法，利用预训练图变换器作为编码器，在嵌入空间中注入虚拟节点作为软提示，以几个有标签的节点进行优化，以调节每个特定的FSNC任务的 node embeddings，通过集成基于图的伪提示演进模块的VNT-GPPE，可以处理基类中标记稀疏的情况，在四个数据集上的实验结果表明，该方法优于现有的最先进方法和完全监督的基线。

Few-shot节点分类的虚拟节点调整