Class imbalance in graph-structured data, where minor classes are significantly underrepresented, poses a critical challenge for Graph Neural Networks (GNNs). To address this challenge, existing studies generally generate new minority nodes and edges connecting new nodes to the original graph to make classes balanced. However, they do not solve the problem that majority classes still propagate information to minority nodes by edges in the original graph which introduces bias towards majority classes. To address this, we introduce BuffGraph, which inserts buffer nodes into the graph, modulating the impact of majority classes to improve minor class representation. Our extensive experiments across diverse real-world datasets empirically demonstrate that BuffGraph outperforms existing baseline methods in class-imbalanced node classification in both natural settings and imbalanced settings. Code is available at https://anonymous.4open.science/r/BuffGraph-730A.

翻译：图结构数据中的类别不平衡问题（少数类样本显著不足）对图神经网络（GNN）构成了关键挑战。为解决该问题，现有研究通常生成新的少数类节点及其与原图连接的边以实现类别平衡。然而，这些方法并未解决多数类通过原图边向少数节点传播信息所引入的偏差问题。为此，我们提出BuffGraph，通过向图中插入缓冲节点来调节多数类的影响，从而提升少数类的表征质量。在多个真实世界数据集上的广泛实验表明，无论在自然设置还是不平衡设置下，BuffGraph在类别不平衡节点分类任务中均优于现有基线方法。代码已发布于https://anonymous.4open.science/r/BuffGraph-730A。