Skip to yearly menu bar Skip to main content


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

Divin Yan · Gengchen Wei · Chen Yang · Shengzhong Zhang · zengfeng Huang

Great Hall & Hall B1+B2 (level 1) #632
[ ]
[ Paper [ Slides [ Poster [ OpenReview
Tue 12 Dec 3:15 p.m. PST — 5:15 p.m. PST


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.

Chat is not available.