  Few-shot node classification is tasked to provide accurate predictions for
nodes from novel classes with only few representative labeled nodes. This
problem has drawn tremendous attention for its projection to prevailing
real-world applications, such as product categorization for newly added
commodity categories on an E-commerce platform with scarce records or diagnoses
for rare diseases on a patient similarity graph. To tackle such challenging
label scarcity issues in the non-Euclidean graph domain, meta-learning has
become a successful and predominant paradigm. More recently, inspired by the
development of graph self-supervised learning, transferring pretrained node
embeddings for few-shot node classification could be a promising alternative to
meta-learning but remains unexposed. In this work, we empirically demonstrate
the potential of an alternative framework, \textit{Transductive Linear
Probing}, that transfers pretrained node embeddings, which are learned from
graph contrastive learning methods. We further extend the setting of few-shot
node classification from standard fully supervised to a more realistic
self-supervised setting, where meta-learning methods cannot be easily deployed
due to the shortage of supervision from training classes. Surprisingly, even
without any ground-truth labels, transductive linear probing with
self-supervised graph contrastive pretraining can outperform the
state-of-the-art fully supervised meta-learning based methods under the same
protocol. We hope this work can shed new light on few-shot node classification
problems and foster future research on learning from scarcely labeled instances
on graphs.
