mlsanityData to 7 Oct 2026

Learning Graph Neural Networks with Noisy Labels

arXiv 1905.01591 ↗First posted 5 May 2019cs.LG stat.MLcited by 29

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Abstract (arXiv)

We study the robustness to symmetric label noise of GNNs training procedures. By combining the nonlinear neural message-passing models (e.g. Graph Isomorphism Networks, GraphSAGE, etc.) with loss correction methods, we present a noise-tolerant approach for the graph classification task. Our experiments show that test accuracy can be improved under the artificial symmetric noisy setting.

Key facts. Cited by 29 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 May 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 3; 2022: 7; 2023: 7; 2024: 6; 2025: 3; 2026: 3.

29 papers.

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  1. 2021NRGNN: Learning a Label Noise-Resistant Graph Neural Network on Sparsely and Noisily Labeled Graphscited by 73
  2. 2024A Survey of Graph Neural Networks in Real world: Imbalance, Noise, Privacy and OOD Challengescited by 54
  3. 2022DropMessage: Unifying Random Dropping for Graph Neural Networkscited by 29
  4. 2022Robust Training of Graph Neural Networks via Noise Governancecited by 29
  5. 2022CLNode: Curriculum Learning for Node Classificationcited by 24
  6. 2023Learning on Graphs under Label Noisecited by 22
  7. 2021Unified Robust Training for Graph NeuralNetworks against Label Noisecited by 21
  8. 2021Noise-robust Graph Learning by Estimating and Leveraging Pairwise Interactionscited by 20
  9. 2022A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protectioncited by 20
  10. 2024NoisyGL: A Comprehensive Benchmark for Graph Neural Networks under Label Noisecited by 16
  11. 2023Towards Data-centric Graph Machine Learning: Review and Outlookcited by 15
  12. 2022Robust Node Classification on Graphs: Jointly from Bayesian Label Transition and Topology-based Label Propagationcited by 12
17 more
  1. 2023ALEX: Towards Effective Graph Transfer Learning with Noisy Labelscited by 11
  2. 2022Recent Advances in Reliable Deep Graph Learning: Inherent Noise, Distribution Shift, and Adversarial Attackcited by 10
  3. 2024Graph Coarsening via Supervised Granular-Ball for Scalable Graph Neural Network Trainingcited by 9
  4. 2023GraphCleaner: Detecting Mislabelled Samples in Popular Graph Learning Benchmarkscited by 7
  5. 2024Mitigating Label Noise on Graph via Topological Sample Selectioncited by 6
  6. 2025Delving into Instance-Dependent Label Noise in Graph Data: A Comprehensive Study and Benchmarkcited by 4
  7. 2022Bayesian Robust Graph Contrastive Learningcited by 2
  8. 2023CSGNN: Conquering Noisy Node labels via Dynamic Class-wise Selectioncited by 1
  9. 2024Graph Neural Networks with Coarse- and Fine-Grained Division for Mitigating Label Sparsity and Noisecited by 1
  10. 2025DeGLIF for Label Noise Robust Node Classification using GNNscited by 1
  11. 2026Identifying and Correcting Label Noise for Robust GNNs via Influence Contradictioncited by 1
  12. 2023Task-Agnostic Graph Neural Network Evaluation via Adversarial Collaborationcited by 0
  13. 2023Resist Label Noise with PGM for Graph Neural Networkscited by 0
  14. 2024Energy Guided smoothness to improve Robustness in Graph Classificationcited by 0
  15. 2025EDN: A Novel Edge-Dependent Noise Model for Graph Datacited by 0
  16. 2026DREAM: Dual-Standard Semantic Homogeneity with Dynamic Optimization for Graph Learning with Label Noisecited by 0
  17. 2026Trapping Attacker in Dilemma: Examining Internal Correlations and External Influences of Trigger for Defending GNN Backdoorscited by 0