# NoisyGL: A Comprehensive Benchmark for Graph Neural Networks under Label Noise

arXiv: https://arxiv.org/abs/2406.04299 · HTML: https://mlsanity.com/paper/2406.04299/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 37 references that are in the map.
- First posted on arXiv 6 Jun 2024.

## Builds on (most cited first, 22 of 22)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels](https://mlsanity.com/paper/1804.06872/) (2018)
- [Pitfalls of Graph Neural Network Evaluation](https://mlsanity.com/paper/1811.05868/) (2018)
- [Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach](https://mlsanity.com/paper/1609.03683/) (2016)
- [Learning from Noisy Labels with Deep Neural Networks: A Survey](https://mlsanity.com/paper/2007.08199/) (2020)
- [Symmetric Cross Entropy for Robust Learning with Noisy Labels](https://mlsanity.com/paper/1908.06112/) (2019)
- [How does Disagreement Help Generalization against Label Corruption?](https://mlsanity.com/paper/1901.04215/) (2019)
- [A critical look at the evaluation of GNNs under heterophily: Are we really making progress?](https://mlsanity.com/paper/2302.11640/) (2023)
- [Combating noisy labels by agreement: A joint training method with co-regularization](https://mlsanity.com/paper/2003.02752/) (2020)
- [Graph Neural Network for Traffic Forecasting: A Survey](https://mlsanity.com/paper/2101.11174/) (2021)
- [Normalized Loss Functions for Deep Learning with Noisy Labels](https://mlsanity.com/paper/2006.13554/) (2020)
- [NRGNN: Learning a Label Noise-Resistant Graph Neural Network on Sparsely and Noisily Labeled Graphs](https://mlsanity.com/paper/2106.04714/) (2021)
- [Learning Graph Neural Networks with Noisy Labels](https://mlsanity.com/paper/1905.01591/) (2019)
- [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)
- [ALEX: Towards Effective Graph Transfer Learning with Noisy Labels](https://mlsanity.com/paper/2309.14673/) (2023)
- [Learnable Graph Convolutional Attention Networks](https://mlsanity.com/paper/2211.11853/) (2022)
- [GraphCleaner: Detecting Mislabelled Samples in Popular Graph Learning Benchmarks](https://mlsanity.com/paper/2306.00015/) (2023)

## Built on by (most cited first, 17 of 17)

- [Nested Graph Pseudo-Label Refinement for Noisy Label Domain Adaptation Learning](https://mlsanity.com/paper/2508.00716/) (2025)
- [Delving into Instance-Dependent Label Noise in Graph Data: A Comprehensive Study and Benchmark](https://mlsanity.com/paper/2506.12468/) (2025)
- [Is the Information Bottleneck Robust Enough? Towards Label-Noise Resistant Information Bottleneck Learning](https://mlsanity.com/paper/2512.10573/) (2025)
- [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)
- [EDN: A Novel Edge-Dependent Noise Model for Graph Data](https://mlsanity.com/paper/2506.11368/) (2025)
- [Commuting Distance Regularization for Timescale-Dependent Label Inconsistency in EEG Emotion Recognition](https://mlsanity.com/paper/2507.10895/) (2025)
- [When Noisy Labels Meet Class Imbalance on Graphs: A Graph Augmentation Method with LLM and Pseudo Label](https://mlsanity.com/paper/2507.18153/) (2025)
- [When LLM Agents Meet Graph Optimization: An Automated Data Quality Improvement Approach](https://mlsanity.com/paper/2510.08952/) (2025)
- [A Framework Based on Graph Cellular Automata for Similarity Evaluation in Urban Spatial Networks](https://mlsanity.com/paper/2511.00768/) (2025)
- [DREAM: Dual-Standard Semantic Homogeneity with Dynamic Optimization for Graph Learning with Label Noise](https://mlsanity.com/paper/2601.17449/) (2026)
- [On the Safety of Graph Representation Learning](https://mlsanity.com/paper/2605.06576/) (2026)
- [UFO: A Unified Flow-Oriented Framework for Robust Continual Graph Learning](https://mlsanity.com/paper/2605.09862/) (2026)
- [OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation](https://mlsanity.com/paper/2607.19108/) (2026)
- [Towards Trustworthy Hypergraph Neural Networks under Label Noise](https://mlsanity.com/paper/2608.04377/) (2026)
- [Beyond Observed Auxiliary Relations: Environment-Conditioned Modeling for Multi-Behavior Recommendation](https://mlsanity.com/paper/2608.22920/) (2026)
- [Particle Competition and Cooperation for Robust Graph Convolutional Network Learning Under Label Noise](https://mlsanity.com/paper/2609.22053/) (2026)

## Abstract (arXiv)

Graph Neural Networks (GNNs) exhibit strong potential in node classification task through a message-passing mechanism. However, their performance often hinges on high-quality node labels, which are challenging to obtain in real-world scenarios due to unreliable sources or adversarial attacks. Consequently, label noise is common in real-world graph data, negatively impacting GNNs by propagating incorrect information during training. To address this issue, the study of Graph Neural Networks under Label Noise (GLN) has recently gained traction. However, due to variations in dataset selection, data splitting, and preprocessing techniques, the community currently lacks a comprehensive benchmark, which impedes deeper understanding and further development of GLN. To fill this gap, we introduce NoisyGL in this paper, the first comprehensive benchmark for graph neural networks under label noise. NoisyGL enables fair comparisons and detailed analyses of GLN methods on noisy labeled graph data across various datasets, with unified experimental settings and interface. Our benchmark has uncovered several important insights that were missed in previous research, and we believe these findings will be highly beneficial for future studies. We hope our open-source benchmark library will foster further advancements in this field. The code of the benchmark can be found in https://github.com/eaglelab-zju/NoisyGL.