Class imbalance is pervasive in real-world graph datasets, where the majority of annotated nodes belong to a small set of classes (majority classes), leaving many other classes (minority classes) with only a handful of labeled nodes. Graph Neural Networks (GNNs) suffer from significant performance degradation in the presence of class imbalance, exhibiting bias towards majority classes and struggling to generalize effectively on minority classes. This limitation stems, in part, from the message passing process, leading GNNs to overfit to the limited neighborhood of annotated nodes from minority classes and impeding the propagation of discriminative information throughout the entire graph. In this paper, we introduce a novel Unified Graph Neural Network Learning (Uni-GNN) framework to tackle class-imbalanced node classification. The proposed framework seamlessly integrates both structural and semantic connectivity representations through semantic and structural node encoders. By combining these connectivity types, Uni-GNN extends the propagation of node embeddings beyond immediate neighbors, encompassing non-adjacent structural nodes and semantically similar nodes, enabling efficient diffusion of discriminative information throughout the graph. Moreover, to harness the potential of unlabeled nodes within the graph, we employ a balanced pseudo-label generation mechanism that augments the pool of available labeled nodes from minority classes in the training set. Experimental results underscore the superior performance of our proposed Uni-GNN framework compared to state-of-the-art class-imbalanced graph learning baselines across multiple benchmark datasets.

本研究针对现实图数据集中普遍存在的类别不平衡问题，提出了一种新的统一图神经网络学习（Uni-GNN）框架。该框架通过语义和结构节点编码器融合结构和语义连接表示，有效扩展了节点嵌入的传播范围，从而提升了对少数类节点的信息传递和分类能力。实验结果显示，Uni-GNN在多个基准数据集上的表现优于现有的类不平衡图学习方法。

克服类别不平衡：具有结构和语义连接表示的统一图神经网络学习