Inductive Linear Probing for Few-shot Node Classification
Hirthik Mathavan Zhen Tan Nivedh Mudiam Huan Liu
Meta-learning has emerged as a powerful training strategy for few-shot node
classification, demonstrating its effectiveness in the transductive setting.
However, the existing literature predominantly focuses on transductive few-shot
node classification, neglecting the widely studied inductive setting in the
broader few-shot learning community. This oversight limits our comprehensive
understanding of the performance of meta-learning based methods on graph data.
In this work, we conduct an empirical study to highlight the limitations of
current frameworks in the inductive few-shot node classification setting.
Additionally, we propose a simple yet competitive baseline approach
specifically tailored for inductive few-shot node classification tasks. We hope
our work can provide a new path forward to better understand how the
meta-learning paradigm works in the graph domain.