  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.
