Graph contrastive learning (GCL) has attracted a surge of attention due to its superior performance for learning node/graph representations without labels. However, in practice, the underlying class distribution of unlabeled nodes for the given graph is usually imbalanced. This highly imbalanced class distribution inevitably deteriorates the quality of learned node representations in GCL. Indeed, we empirically find that most state-of-the-art GCL methods cannot obtain discriminative representations and exhibit poor performance on imbalanced node classification. Motivated by this observation, we propose a principled GCL framework on Imbalanced node classification (ImGCL), which automatically and adaptively balances the representations learned from GCL without labels. Specifically, we first introduce the online clustering based progressively balanced sampling (PBS) method with theoretical rationale, which balances the training sets based on pseudo-labels obtained from learned representations in GCL. We then develop the node centrality based PBS method to better preserve the intrinsic structure of graphs, by upweighting the important nodes of the given graph. Extensive experiments on multiple imbalanced graph datasets and imbalanced settings demonstrate the effectiveness of our proposed framework, which significantly improves the performance of the recent state-of-the-art GCL methods. Further experimental ablations and analyses show that the ImGCL framework consistently improves the representation quality of nodes in under-represented (tail) classes.

翻译：图对比学习（GCL）因其无需标签即可学习节点/图表示的性能优势而备受关注。然而在实际中，给定图中无标签节点的潜在类别分布通常是不平衡的。这种高度不平衡的类别分布不可避免地降低了GCL所学节点表示的质量。事实上，我们通过实验发现，大多数最新GCL方法无法获得区分性表示，且在不平衡节点分类上表现不佳。基于此观察，我们提出一种针对不平衡节点分类的规范化GCL框架（ImGCL），该框架能够自动自适应地平衡从GCL中学习的无标签表示。具体而言，我们首先引入基于在线聚类的渐进平衡采样（PBS）方法及其理论依据，该方法根据GCL所学表示获得的伪标签来平衡训练集。随后，我们开发了基于节点中心性的PBS方法，通过提升给定图中重要节点的权重，更好地保留图的固有结构。在多个不平衡图数据集及不平衡场景下的大量实验表明，我们提出的框架具有有效性，显著提升了最新GCL方法的性能。进一步的消融实验与分析显示，ImGCL框架能够持续提升欠表示（尾部）类别节点的表示质量。