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.

翻译：本文提出了一种新方法，旨在解决图神经网络（GNN）在图结构数据学习中面临的类别不平衡问题。我们的方法将不平衡节点分类与偏差-方差分解相结合，构建了一个将数据不平衡与模型方差紧密关联的理论框架。同时，我们利用图增强技术估计方差，并设计了一个正则化项以减轻不平衡的影响。在多个基准数据集上进行了详尽的测试，包括自然不平衡数据集和公共划分的类别不平衡数据集，结果表明我们的方法在各种不平衡场景下均优于现有最先进方法。本研究为解决GNN中的不平衡节点分类问题提供了新的理论视角。