  This paper introduces a new approach to address the issue of class imbalance
in graph neural networks (GNNs) for learning on graph-structured data. Our
approach integrates imbalanced node classification and Bias-Variance
Decomposition, establishing a theoretical framework that closely relates data
imbalance to model variance. We also leverage graph augmentation technique to
estimate the variance, and design a regularization term to alleviate the impact
of imbalance. Exhaustive tests are conducted on multiple benchmarks, including
naturally imbalanced datasets and public-split class-imbalanced datasets,
demonstrating that our approach outperforms state-of-the-art methods in various
imbalanced scenarios. This work provides a novel theoretical perspective for
addressing the problem of imbalanced node classification in GNNs.
