Abstract
Prevailing deep graph learning models often suffer from label sparsity issue.
Although many graph few-shot learning (GFL) methods have been developed to
avoid performance degradation in face of limited annotated data, they
excessively rely on labeled data, where the distribution shift in the test
phase might result in impaired generalization ability. Additionally, they lack
a general purpose as their designs are coupled with task or data-specific
characteristics. To this end, we propose a general and effective Contrastive
Graph Few-shot Learning framework (CGFL). CGFL leverages a self-distilled
contrastive learning procedure to boost GFL. Specifically, our model firstly
pre-trains a graph encoder with contrastive learning using unlabeled data.
Later, the trained encoder is frozen as a teacher model to distill a student
model with a contrastive loss. The distilled model is finally fed to GFL. CGFL
learns data representation in a self-supervised manner, thus mitigating the
distribution shift impact for better generalization and making model task and
data-independent for a general graph mining purpose. Furthermore, we introduce
an information-based method to quantitatively measure the capability of CGFL.
Comprehensive experiments demonstrate that CGFL outperforms state-of-the-art
baselines on several graph mining tasks in the few-shot scenario. We also
provide quantitative measurement of CGFL's success.