Rethinking Semi-Supervised Imbalanced Node Classification from Bias-Variance Decomposition

Divin Yan, Gengchen Wei, Chen Yang, Shengzhong Zhang, Zengfeng Huang

Keywords: Imbalanced Node Classification, Bias-Variance Decomposition, Graph Neural Networks

TL;DR: A novel framework regularizes the model’s variance, introducing a new perspective for semi-supervised imbalanced node classification.

Abstract: This paper introduces a new approach to address the issue of class imbalance in graph neural networks (GNNs) for learning on graph-structured data. Our approach integrates imbalanced node classification and Bias-Variance Decomposition, establishing a theoretical framework that closely relates data imbalance to model variance. We also leverage graph augmentation technique to estimate the variance and design a regularization term to alleviate the impact of imbalance. Exhaustive tests are conducted on multiple benchmarks, including naturally imbalanced datasets and public-split class-imbalanced datasets, demonstrating that our approach outperforms state-of-the-art methods in various imbalanced scenarios. This work provides a novel theoretical perspective for addressing the problem of imbalanced node classification in GNNs.

Submission Number: 7967