Class imbalance is prevalent in real-world node classification tasks and
often biases graph learning models toward majority classes. Most existing
studies root from a node-centric perspective and aim to address the class
imbalance in training data by node/class-wise reweighting or resampling. In
this paper, we approach the source of the class-imbalance bias from an
under-explored topology-centric perspective. Our investigation reveals that
beyond the inherently skewed training class distribution, the graph topology
also plays an important role in the formation of predictive bias: we identify
two fundamental challenges, namely ambivalent and distant message-passing, that
can exacerbate the bias by aggravating majority-class over-generalization and
minority-class misclassification. In light of these findings, we devise a
lightweight topological augmentation method ToBA to dynamically rectify the
nodes influenced by ambivalent/distant message-passing during graph learning,
so as to mitigate the class-imbalance bias. We highlight that ToBA is a
model-agnostic, efficient, and versatile solution that can be seamlessly
combined with and further boost other imbalance-handling techniques. Systematic
experiments validate the superior performance of ToBA in both promoting
imbalanced node classification and mitigating the prediction bias between
different classes.

该研究针对真实世界的节点分类任务中普遍存在的类别不平衡问题，从节点中心和拓扑结构的角度出发，通过拓扑增强方法解决了类别不平衡的问题，并在促进不平衡节点分类和缓解不同类别之间的预测偏差方面验证了其卓越性能。