# Learning Graph Neural Networks with Noisy Labels

arXiv: https://arxiv.org/abs/1905.01591 · HTML: https://mlsanity.com/paper/1905.01591/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 May 2019.

## Built on by (most cited first, 30 of 30)

- [NRGNN: Learning a Label Noise-Resistant Graph Neural Network on Sparsely and Noisily Labeled Graphs](https://mlsanity.com/paper/2106.04714/) (2021)
- [A Survey of Graph Neural Networks in Real world: Imbalance, Noise, Privacy and OOD Challenges](https://mlsanity.com/paper/2403.04468/) (2024)
- [DropMessage: Unifying Random Dropping for Graph Neural Networks](https://mlsanity.com/paper/2204.10037/) (2022)
- [Robust Training of Graph Neural Networks via Noise Governance](https://mlsanity.com/paper/2211.06614/) (2022)
- [CLNode: Curriculum Learning for Node Classification](https://mlsanity.com/paper/2206.07258/) (2022)
- [Learning on Graphs under Label Noise](https://mlsanity.com/paper/2306.08194/) (2023)
- [Unified Robust Training for Graph NeuralNetworks against Label Noise](https://mlsanity.com/paper/2103.03414/) (2021)
- [Noise-robust Graph Learning by Estimating and Leveraging Pairwise Interactions](https://mlsanity.com/paper/2106.07451/) (2021)
- [A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection](https://mlsanity.com/paper/2205.10014/) (2022)
- [NoisyGL: A Comprehensive Benchmark for Graph Neural Networks under Label Noise](https://mlsanity.com/paper/2406.04299/) (2024)
- [Towards Data-centric Graph Machine Learning: Review and Outlook](https://mlsanity.com/paper/2309.10979/) (2023)
- [Robust Node Classification on Graphs: Jointly from Bayesian Label Transition and Topology-based Label Propagation](https://mlsanity.com/paper/2208.09779/) (2022)
- [ALEX: Towards Effective Graph Transfer Learning with Noisy Labels](https://mlsanity.com/paper/2309.14673/) (2023)
- [Recent Advances in Reliable Deep Graph Learning: Inherent Noise, Distribution Shift, and Adversarial Attack](https://mlsanity.com/paper/2202.07114/) (2022)
- [Graph Coarsening via Supervised Granular-Ball for Scalable Graph Neural Network Training](https://mlsanity.com/paper/2412.13842/) (2024)
- [GraphCleaner: Detecting Mislabelled Samples in Popular Graph Learning Benchmarks](https://mlsanity.com/paper/2306.00015/) (2023)
- [Mitigating Label Noise on Graph via Topological Sample Selection](https://mlsanity.com/paper/2403.01942/) (2024)
- [Delving into Instance-Dependent Label Noise in Graph Data: A Comprehensive Study and Benchmark](https://mlsanity.com/paper/2506.12468/) (2025)
- [Bayesian Robust Graph Contrastive Learning](https://mlsanity.com/paper/2205.14109/) (2022)
- [CSGNN: Conquering Noisy Node labels via Dynamic Class-wise Selection](https://mlsanity.com/paper/2311.11473/) (2023)
- [Graph Neural Networks with Coarse- and Fine-Grained Division for Mitigating Label Sparsity and Noise](https://mlsanity.com/paper/2411.03744/) (2024)
- [DeGLIF for Label Noise Robust Node Classification using GNNs](https://mlsanity.com/paper/2506.00244/) (2025)
- [Identifying and Correcting Label Noise for Robust GNNs via Influence Contradiction](https://mlsanity.com/paper/2601.17469/) (2026)
- [Task-Agnostic Graph Neural Network Evaluation via Adversarial Collaboration](https://mlsanity.com/paper/2301.11517/) (2023)
- [Resist Label Noise with PGM for Graph Neural Networks](https://mlsanity.com/paper/2311.02116/) (2023)
- [Energy Guided smoothness to improve Robustness in Graph Classification](https://mlsanity.com/paper/2412.08419/) (2024)
- [EDN: A Novel Edge-Dependent Noise Model for Graph Data](https://mlsanity.com/paper/2506.11368/) (2025)
- [DREAM: Dual-Standard Semantic Homogeneity with Dynamic Optimization for Graph Learning with Label Noise](https://mlsanity.com/paper/2601.17449/) (2026)
- [Trapping Attacker in Dilemma: Examining Internal Correlations and External Influences of Trigger for Defending GNN Backdoors](https://mlsanity.com/paper/2605.08278/) (2026)
- [Towards Trustworthy Hypergraph Neural Networks under Label Noise](https://mlsanity.com/paper/2608.04377/) (2026)

## 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.