Class-Imbalanced Graph Learning without Class Rebalancing
Fuente:
arXiv
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| Autores principales: | , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866916250761822208 |
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| author | Liu, Zhining Qiu, Ruizhong Zeng, Zhichen Yoo, Hyunsik Zhou, David Xu, Zhe Zhu, Yada Weldemariam, Kommy He, Jingrui Tong, Hanghang |
| author_facet | Liu, Zhining Qiu, Ruizhong Zeng, Zhichen Yoo, Hyunsik Zhou, David Xu, Zhe Zhu, Yada Weldemariam, Kommy He, Jingrui Tong, Hanghang |
| contents | Class imbalance is prevalent in real-world node classification tasks and poses great challenges for graph learning models. Most existing studies are rooted in a class-rebalancing (CR) perspective and address class imbalance with class-wise reweighting or resampling. In this work, we approach the root cause of class-imbalance bias from an topological paradigm. Specifically, we theoretically reveal two fundamental phenomena in the graph topology that greatly exacerbate the predictive bias stemming from class imbalance. On this basis, we devise a lightweight topological augmentation framework BAT to mitigate the class-imbalance bias without class rebalancing. Being orthogonal to CR, BAT can function as an efficient plug-and-play module that can be seamlessly combined with and significantly boost existing CR techniques. Systematic experiments on real-world imbalanced graph learning tasks show that BAT can deliver up to 46.27% performance gain and up to 72.74% bias reduction over existing techniques. Code, examples, and documentations are available at https://github.com/ZhiningLiu1998/BAT. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_14181 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Class-Imbalanced Graph Learning without Class Rebalancing Liu, Zhining Qiu, Ruizhong Zeng, Zhichen Yoo, Hyunsik Zhou, David Xu, Zhe Zhu, Yada Weldemariam, Kommy He, Jingrui Tong, Hanghang Machine Learning Artificial Intelligence Class imbalance is prevalent in real-world node classification tasks and poses great challenges for graph learning models. Most existing studies are rooted in a class-rebalancing (CR) perspective and address class imbalance with class-wise reweighting or resampling. In this work, we approach the root cause of class-imbalance bias from an topological paradigm. Specifically, we theoretically reveal two fundamental phenomena in the graph topology that greatly exacerbate the predictive bias stemming from class imbalance. On this basis, we devise a lightweight topological augmentation framework BAT to mitigate the class-imbalance bias without class rebalancing. Being orthogonal to CR, BAT can function as an efficient plug-and-play module that can be seamlessly combined with and significantly boost existing CR techniques. Systematic experiments on real-world imbalanced graph learning tasks show that BAT can deliver up to 46.27% performance gain and up to 72.74% bias reduction over existing techniques. Code, examples, and documentations are available at https://github.com/ZhiningLiu1998/BAT. |
| title | Class-Imbalanced Graph Learning without Class Rebalancing |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2308.14181 |