Rethinking Semi-Supervised Imbalanced Node Classification from Bias-Variance Decomposition
Divin Yan, Gengchen Wei, Chen Yang, Shengzhong Zhang, Zengfeng Huang
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.
BibTeX
@inproceedings{
yan2023rethinking,
title={Rethinking Semi-Supervised Imbalanced Node Classification from Bias-Variance Decomposition},
author={Divin Yan and Gengchen Wei and Chen Yang and Shengzhong Zhang and Zengfeng Huang},
booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
year={2023},
url={https://openreview.net/forum?id=0gvtoxhvMY}
}