Topological Augmentation for Class-Imbalanced Node Classification
Zhining Liu Zhichen Zeng Ruizhong Qiu Hyunsik Yoo David Zhou Zhe Xu Yada Zhu Kommy Weldemariam Jingrui He Hanghang Tong
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.