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.

本文提出了一种新方法来解决图神经网络（GNNs）中的类别不平衡问题，通过整合不平衡节点分类和偏差 - 方差分解的理论框架，利用图增强技术估计方差，并设计了正则化项来减轻不平衡的影响，通过在多个基准测试中广泛测试，包括自然不平衡数据集和公共分割类别不平衡数据集，证明了我们的方法在各种不平衡场景中优于现有方法。这项工作为解决图神经网络中的不平衡节点分类问题提供了一种新的理论视角。