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

Published: 21 Sept 2023, Last Modified: 05 Dec 2023NeurIPS 2023 posterEveryoneRevisionsBibTeX
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.
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Submission Number: 7967