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UPL: Uncertainty-aware Pseudo-labeling for Imbalance Transductive Node Classification

arXiv 2502.00716 ↗First posted 2 Feb 2025cs.LGcited by 0

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Abstract (arXiv)

Graph-structured datasets often suffer from class imbalance, which complicates node classification tasks. In this work, we address this issue by first providing an upper bound on population risk for imbalanced transductive node classification. We then propose a simple and novel algorithm, Uncertainty-aware Pseudo-labeling (UPL). Our approach leverages pseudo-labels assigned to unlabeled nodes to mitigate the adverse effects of imbalance on classification accuracy. Furthermore, the UPL algorithm enhances the accuracy of pseudo-labeling by reducing training noise of pseudo-labels through a novel uncertainty-aware approach. We comprehensively evaluate the UPL algorithm across various benchmark datasets, demonstrating its superior performance compared to existing state-of-the-art methods.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 39 of its 75 references that are in the map. First posted on arXiv 2 Feb 2025.

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39 of 75 references.

References · 39

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27 more
  1. 2021FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labelingcited by 537
  2. 2019Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learningcited by 507
  3. 2020Balanced Meta-Softmax for Long-Tailed Visual Recognitioncited by 397
  4. 2020Meta Pseudo Labelscited by 372
  5. 2021In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learningcited by 301
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  15. 2017Active Learning for Graph Embeddingcited by 53
  16. 2021Learning Theory Can (Sometimes) Explain Generalisation in Graph Neural Networkscited by 51
  17. 2022TAM: Topology-Aware Margin Loss for Class-Imbalanced Node Classificationcited by 41
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  19. 2022Confidence May Cheat: Self-Training on Graph Neural Networks under Distribution Shiftcited by 38
  20. 2020Optimization and Generalization Analysis of Transduction through Gradient Boosting and Application to Multi-scale Graph Neural Networkscited by 31
  21. 2014Transductive Rademacher Complexity and its Applicationscited by 25
  22. 2023Class-Imbalanced Learning on Graphs: A Surveycited by 24
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  25. 2023Rethinking Semi-Supervised Imbalanced Node Classification from Bias-Variance Decompositioncited by 13
  26. 2020Self-Enhanced GNN: Improving Graph Neural Networks Using Model Outputscited by 11
  27. 2023Geometric Imbalance in Semi-Supervised Node Classificationcited by 5

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