Class-Imbalanced Graph Learning without Class Rebalancing
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 roo...
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Main Authors | , , , , , , , , , |
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Format | Journal Article |
Language | English |
Published |
27.08.2023
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Subjects | |
Online Access | Get full text |
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Summary: | 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. |
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DOI: | 10.48550/arxiv.2308.14181 |