Imbalanced node classification is a critical challenge in graph learning, where most existing methods typically utilize Graph Neural Networks (GNNs) to learn node representations. These methods can be broadly categorized into the data-level and the algorithm-level. The former aims to synthesize minority-class nodes to mitigate quantity imbalance, while the latter tries to optimize the learning process to highlight minority classes. However, neither of them addresses the inherently imbalanced graph structure, which is a fundamental factor that incurs majority-class dominance and minority-class assimilation in GNNs. Our theoretical analysis further supports this critical insight. Therefore, we propose GraphSB (Graph Structural Balance), a novel framework that incorporates Structural Balance as a key strategy to address the underlying imbalanced graph structure before node synthesis. Structural Balance performs a two-stage structure optimization: Structure Enhancement that mines hard samples near decision boundaries through dual-view analysis and enhances connectivity for minority classes through adaptive augmentation, and Relation Diffusion that propagates the enhanced minority context while simultaneously capturing higher-order structural dependencies. Thus, GraphSB balances structural distribution before node synthesis, enabling more effective learning in GNNs. Extensive experiments demonstrate that GraphSB significantly outperforms the state-of-the-art methods. More importantly, the proposed Structural Balance can be seamlessly integrated into state-of-the-art methods as a simple plug-and-play module, increasing their accuracy by an average of 4.57%.

翻译：不平衡节点分类是图学习中的一个关键挑战，现有方法大多利用图神经网络（GNNs）学习节点表示。这些方法可大致分为数据层面和算法层面两类：前者旨在合成少数类节点以缓解数量不平衡，后者则尝试优化学习过程以突出少数类。然而，两者均未解决固有的不平衡图结构问题，而这正是导致GNNs中多数类主导和少数类同化的根本因素。我们的理论分析进一步支持了这一关键见解。因此，我们提出GraphSB（图结构平衡），一种将结构平衡作为关键策略的新框架，以在节点合成前解决潜在的不平衡图结构问题。结构平衡执行两阶段结构优化：结构增强通过双视角分析挖掘决策边界附近的困难样本，并通过自适应增强提升少数类的连通性；关系扩散则传播增强后的少数类上下文，同时捕获高阶结构依赖关系。因此，GraphSB在节点合成前平衡了结构分布，使GNNs能够进行更有效的学习。大量实验表明，GraphSB显著优于现有最先进方法。更重要的是，所提出的结构平衡模块可作为即插即用组件无缝集成到现有最先进方法中，平均提升其准确率4.57%。