Rethinking Semi-Supervised Imbalanced Node Classification from Bias-Variance Decomposition
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866910842888388608 |
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| author | Yan, Liang Wei, Gengchen Yang, Chen Zhang, Shengzhong Huang, Zengfeng |
| author_facet | Yan, Liang Wei, Gengchen Yang, Chen Zhang, Shengzhong Huang, Zengfeng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_18765 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Rethinking Semi-Supervised Imbalanced Node Classification from Bias-Variance Decomposition Yan, Liang Wei, Gengchen Yang, Chen Zhang, Shengzhong Huang, Zengfeng Machine Learning 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. |
| title | Rethinking Semi-Supervised Imbalanced Node Classification from Bias-Variance Decomposition |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2310.18765 |