mlsanityData to 7 Oct 2026

Rethinking Semi-Supervised Imbalanced Node Classification from Bias-Variance Decomposition

arXiv 2310.18765 ↗First posted 28 Oct 2023cs.LGcited by 13

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Abstract (arXiv)

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.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 40 of its 56 references that are in the map. First posted on arXiv 28 Oct 2023.

Builds on

40 of 56 references.

References · 40

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28 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 6; 2026: 5.

13 papers.

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  1. 2024DIVE: Subgraph Disagreement for Graph Out-of-Distribution Generalizationcited by 7
  2. 2024Foundations and Frontiers of Graph Learning Theorycited by 3
  3. 2025NodeReg: Mitigating the Imbalance and Distribution Shift Effects in Semi-Supervised Node Classification via Norm Consistencycited by 1
  4. 2025GraphIFE: Rethinking Graph Imbalance Node Classification via Invariant Learningcited by 1
  5. 2026Rel-MOSS: Towards Imbalanced Relational Deep Learning on Relational Databasescited by 1
  6. 2025UPL: Uncertainty-aware Pseudo-labeling for Imbalance Transductive Node Classificationcited by 0
  7. 2025Pure Node Selection for Imbalanced Graph Node Classificationcited by 0
  8. 2025FUSE: Fast Semi-Supervised Node Embedding Learning via Structural and Label-Aware Optimizationcited by 0
  9. 2025GraphSB: Boosting Imbalanced Node Classification on Graphs through Structural Balancecited by 0
  10. 2026GraphSB: Boosting Imbalanced Node Classification on Graphs through Structural Balancecited by 0
  11. 2026PIMPC-GNN: Physics-Informed Multi-Phase Consensus Learning for Enhancing Imbalanced Node Classification in Graph Neural Networkscited by 0
  12. 2026Enhancing Imbalanced Node Classification via Curriculum-Guided Feature Learning and Three-Stage Attention Networkcited by 0
1 more
  1. 2026NodeImport: Imbalanced Node Classification with Node Importance Assessmentcited by 0