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Updated: Jun 9, 2025

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Imbalanced graph learning via mixed entropy minimization.

Liwen Xu1, Huaguang Zhu1, Jiali Chen2

  • 1College of Science, North China University of Technology, Beijing, 100144, China.

Scientific Reports
|October 22, 2024
PubMed
Summary
This summary is machine-generated.

GraphME introduces mixed entropy minimization for imbalanced graph node classification. This novel approach enhances accuracy and robustness without complex oversampling, offering a free imbalance defense.

Keywords:
Graph learningImbalanced node classificationMixed entropy minimization (ME)

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Area of Science:

  • Graph machine learning
  • Imbalanced learning

Background:

  • Node classification on imbalanced graphs is challenging.
  • Traditional oversampling methods complicate training.
  • Existing methods may compromise efficiency or robustness.

Purpose of the Study:

  • Introduce a novel training paradigm for node classification on imbalanced graphs.
  • Develop a method that provides 'free imbalance defense' without additional steps.
  • Enhance classification accuracy and robustness efficiently.

Main Methods:

  • Propose GraphME, a method based on mixed entropy minimization (ME).
  • ME maximizes correct class probabilities and minimizes incorrect ones via a guidance term.
  • Integrate ME with adversarial training techniques.

Main Results:

  • GraphME consistently outperforms the traditional cross-entropy objective on multiple datasets.
  • Demonstrates enhanced robustness and classification accuracy.
  • Achieves significant improvements without compromising efficiency.

Conclusions:

  • GraphME offers an effective and efficient solution for node classification on imbalanced graphs.
  • The method provides a robust 'free imbalance defense'.
  • GraphME can be seamlessly integrated with adversarial training for further robustness gains.