# Learning on Graphs under Label Noise

arXiv: https://arxiv.org/abs/2306.08194 · HTML: https://mlsanity.com/paper/2306.08194/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 28 references that are in the map.
- First posted on arXiv 14 Jun 2023.

## Builds on (most cited first, 19 of 19)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Graph Contrastive Learning with Augmentations](https://mlsanity.com/paper/2010.13902/) (2020)
- [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)
- [Graph Contrastive Learning with Adaptive Augmentation](https://mlsanity.com/paper/2010.14945/) (2020)
- [How does Disagreement Help Generalization against Label Corruption?](https://mlsanity.com/paper/1901.04215/) (2019)
- [Graph Structure Learning for Robust Graph Neural Networks](https://mlsanity.com/paper/2005.10203/) (2020)
- [Graph Convolutional Label Noise Cleaner: Train a Plug-and-play Action Classifier for Anomaly Detection](https://mlsanity.com/paper/1903.07256/) (2019)
- [Learning with Instance-Dependent Label Noise: A Sample Sieve Approach](https://mlsanity.com/paper/2010.02347/) (2020)
- [InfoGCL: Information-Aware Graph Contrastive Learning](https://mlsanity.com/paper/2110.15438/) (2021)
- [A Second-Order Approach to Learning with Instance-Dependent Label Noise](https://mlsanity.com/paper/2012.11854/) (2020)
- [NRGNN: Learning a Label Noise-Resistant Graph Neural Network on Sparsely and Noisily Labeled Graphs](https://mlsanity.com/paper/2106.04714/) (2021)
- [Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning](https://mlsanity.com/paper/2105.05682/) (2021)
- [Towards Robust Graph Neural Networks for Noisy Graphs with Sparse Labels](https://mlsanity.com/paper/2201.00232/) (2022)
- [Learning Graph Neural Networks with Noisy Labels](https://mlsanity.com/paper/1905.01591/) (2019)
- [GLCC: A General Framework for Graph-Level Clustering](https://mlsanity.com/paper/2210.11879/) (2022)
- [Towards an Efficient and General Framework of Robust Training for Graph Neural Networks](https://mlsanity.com/paper/2002.10947/) (2020)

## Built on by (most cited first, 22 of 22)

- [A Survey of Graph Neural Networks in Real world: Imbalance, Noise, Privacy and OOD Challenges](https://mlsanity.com/paper/2403.04468/) (2024)
- [Towards Graph Contrastive Learning: A Survey and Beyond](https://mlsanity.com/paper/2405.11868/) (2024)
- [NoisyGL: A Comprehensive Benchmark for Graph Neural Networks under Label Noise](https://mlsanity.com/paper/2406.04299/) (2024)
- [Resurrecting Label Propagation for Graphs with Heterophily and Label Noise](https://mlsanity.com/paper/2310.16560/) (2023)
- [ALEX: Towards Effective Graph Transfer Learning with Noisy Labels](https://mlsanity.com/paper/2309.14673/) (2023)
- [Nested Graph Pseudo-Label Refinement for Noisy Label Domain Adaptation Learning](https://mlsanity.com/paper/2508.00716/) (2025)
- [Redundancy-Free Self-Supervised Relational Learning for Graph Clustering](https://mlsanity.com/paper/2309.04694/) (2023)
- [Mitigating Label Noise on Graph via Topological Sample Selection](https://mlsanity.com/paper/2403.01942/) (2024)
- [Towards Long-Tailed Recognition for Graph Classification via Collaborative Experts](https://mlsanity.com/paper/2308.16609/) (2023)
- [Rethinking the impact of noisy labels in graph classification: A utility and privacy perspective](https://mlsanity.com/paper/2406.07314/) (2024)
- [Is the Information Bottleneck Robust Enough? Towards Label-Noise Resistant Information Bottleneck Learning](https://mlsanity.com/paper/2512.10573/) (2025)
- [COOL: A Conjoint Perspective on Spatio-Temporal Graph Neural Network for Traffic Forecasting](https://mlsanity.com/paper/2403.01091/) (2024)
- [Graph Contrastive Learning with Low-Rank Regularization and Low-Rank Attention for Noisy Node Classification](https://mlsanity.com/paper/2402.09600/) (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)
- [Graph Partial Label Learning with Potential Cause Discovering](https://mlsanity.com/paper/2403.11449/) (2024)
- [Energy Guided smoothness to improve Robustness in Graph Classification](https://mlsanity.com/paper/2412.08419/) (2024)
- [Weak Supervision for Real World Graphs](https://mlsanity.com/paper/2506.02451/) (2025)
- [EDN: A Novel Edge-Dependent Noise Model for Graph Data](https://mlsanity.com/paper/2506.11368/) (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)
- [DREAM: Dual-Standard Semantic Homogeneity with Dynamic Optimization for Graph Learning with Label Noise](https://mlsanity.com/paper/2601.17449/) (2026)
- [PaSta: Noisy Node Classification with Partial Label Learning](https://mlsanity.com/paper/2608.25365/) (2026)

## Abstract (arXiv)

Node classification on graphs is a significant task with a wide range of applications, including social analysis and anomaly detection. Even though graph neural networks (GNNs) have produced promising results on this task, current techniques often presume that label information of nodes is accurate, which may not be the case in real-world applications. To tackle this issue, we investigate the problem of learning on graphs with label noise and develop a novel approach dubbed Consistent Graph Neural Network (CGNN) to solve it. Specifically, we employ graph contrastive learning as a regularization term, which promotes two views of augmented nodes to have consistent representations. Since this regularization term cannot utilize label information, it can enhance the robustness of node representations to label noise. Moreover, to detect noisy labels on the graph, we present a sample selection technique based on the homophily assumption, which identifies noisy nodes by measuring the consistency between the labels with their neighbors. Finally, we purify these confident noisy labels to permit efficient semantic graph learning. Extensive experiments on three well-known benchmark datasets demonstrate the superiority of our CGNN over competing approaches.