  Graph contrastive learning (GCL) has attracted a surge of attention due to
its superior performance for learning node/graph representations without
labels. However, in practice, the underlying class distribution of unlabeled
nodes for the given graph is usually imbalanced. This highly imbalanced class
distribution inevitably deteriorates the quality of learned node
representations in GCL. Indeed, we empirically find that most state-of-the-art
GCL methods cannot obtain discriminative representations and exhibit poor
performance on imbalanced node classification. Motivated by this observation,
we propose a principled GCL framework on Imbalanced node classification
(ImGCL), which automatically and adaptively balances the representations
learned from GCL without labels. Specifically, we first introduce the online
clustering based progressively balanced sampling (PBS) method with theoretical
rationale, which balances the training sets based on pseudo-labels obtained
from learned representations in GCL. We then develop the node centrality based
PBS method to better preserve the intrinsic structure of graphs, by upweighting
the important nodes of the given graph. Extensive experiments on multiple
imbalanced graph datasets and imbalanced settings demonstrate the effectiveness
of our proposed framework, which significantly improves the performance of the
recent state-of-the-art GCL methods. Further experimental ablations and
analyses show that the ImGCL framework consistently improves the representation
quality of nodes in under-represented (tail) classes.
