Graph Contrastive Learning Meets Graph Meta Learning: A Unified Method
  for Few-shot Node Tasks
Hao Liu Jiarui Feng Lecheng Kong Dacheng Tao Yixin Chen Muhan Zhang
Graph Neural Networks (GNNs) have become popular in Graph Representation
Learning (GRL). One fundamental application is few-shot node classification.
Most existing methods follow the meta learning paradigm, showing the ability of
fast generalization to few-shot tasks. However, recent works indicate that
graph contrastive learning combined with fine-tuning can significantly
outperform meta learning methods. Despite the empirical success, there is
limited understanding of the reasons behind it. In our study, we first identify
two crucial advantages of contrastive learning compared to meta learning,
including (1) the comprehensive utilization of graph nodes and (2) the power of
graph augmentations. To integrate the strength of both contrastive learning and
meta learning on the few-shot node classification tasks, we introduce a new
paradigm: Contrastive Few-Shot Node Classification (COLA). Specifically, COLA
employs graph augmentations to identify semantically similar nodes, which
enables the construction of meta-tasks without the need for label information.
Therefore, COLA can utilize all nodes to construct meta-tasks, further reducing
the risk of overfitting. Through extensive experiments, we validate the
essentiality of each component in our design and demonstrate that COLA achieves
new state-of-the-art on all tasks.