mlsanityData to 14 Oct 2026

Learn Beneficial Noise as Graph Augmentation

arXiv 2505.19024 ↗First posted 25 May 2025cs.LG stat.MLcited by 15

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Abstract (arXiv)

Although graph contrastive learning (GCL) has been widely investigated, it is still a challenge to generate effective and stable graph augmentations. Existing methods often apply heuristic augmentation like random edge dropping, which may disrupt important graph structures and result in unstable GCL performance. In this paper, we propose Positive-incentive Noise driven Graph Data Augmentation (PiNGDA), where positive-incentive noise (pi-noise) scientifically analyzes the beneficial effect of noise under the information theory. To bridge the standard GCL and pi-noise framework, we design a Gaussian auxiliary variable to convert the loss function to information entropy. We prove that the standard GCL with pre-defined augmentations is equivalent to estimate the beneficial noise via the point estimation. Following our analysis, PiNGDA is derived from learning the beneficial noise on both topology and attributes through a trainable noise generator for graph augmentations, instead of the simple estimation. Since the generator learns how to produce beneficial perturbations on graph topology and node attributes, PiNGDA is more reliable compared with the existing methods. Extensive experimental results validate the effectiveness and stability of PiNGDA.

Key facts. Cited by 15 papers in the map (data through 14 Oct 2026). Builds on 38 of its 53 references that are in the map. First posted on arXiv 25 May 2025.

Builds on

38 of 53 references.

References · 38

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  2. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 12,049
  3. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  4. 2018Representation Learning with Contrastive Predictive Codingcited by 7,777
  5. 2017Inductive Representation Learning on Large Graphscited by 6,320
  6. 2018How Powerful are Graph Neural Networks?cited by 4,393
  7. 2021SimCSE: Simple Contrastive Learning of Sentence Embeddingscited by 2,173
  8. 2020Open Graph Benchmark: Datasets for Machine Learning on Graphscited by 2,163
  9. 2016node2vec: Scalable Feature Learning for Networkscited by 1,918
  10. 2014DeepWalk: Online Learning of Social Representationscited by 1,547
  11. 2019Simplifying Graph Convolutional Networkscited by 1,477
  12. 2020Graph Contrastive Learning with Augmentationscited by 1,208
26 more
  1. 2018Pitfalls of Graph Neural Network Evaluationcited by 1,007
  2. 2020Geom-GCN: Geometric Graph Convolutional Networkscited by 826
  3. 2019Learning Representations by Maximizing Mutual Information Across Viewscited by 734
  4. 2020TUDataset: A collection of benchmark datasets for learning with graphscited by 728
  5. 2019DropEdge: Towards Deep Graph Convolutional Networks on Node Classificationcited by 680
  6. 2020Contrastive Multi-View Representation Learning on Graphscited by 652
  7. 2020Deep Graph Contrastive Representation Learningcited by 539
  8. 2020Graph Contrastive Learning with Adaptive Augmentationcited by 519
  9. 2017Representation Learning on Graphs: Methods and Applicationscited by 457
  10. 2021Graph Contrastive Learning Automatedcited by 287
  11. 2020Wiki-CS: A Wikipedia-Based Benchmark for Graph Neural Networkscited by 260
  12. 2021ConSERT: A Contrastive Framework for Self-Supervised Sentence Representation Transfercited by 231
  13. 2020Graph Representation Learning via Graphical Mutual Information Maximizationcited by 217
  14. 2021Adversarial Graph Augmentation to Improve Graph Contrastive Learningcited by 200
  15. 2021Large-Scale Representation Learning on Graphs via Bootstrappingcited by 186
  16. 2020Towards Domain-Agnostic Contrastive Learningcited by 75
  17. 2022Beyond Smoothing: Unsupervised Graph Representation Learning with Edge Heterophily Discriminatingcited by 57
  18. 2020A Framework For Contrastive Self-Supervised Learning And Designing A New Approachcited by 52
  19. 2022Positive-incentive Noisecited by 39
  20. 2023Variational Positive-incentive Noise: How Noise Benefits Modelscited by 38
  21. 2024Enhance Vision-Language Alignment with Noisecited by 34
  22. 2023Neighbor Contrastive Learning on Learnable Graph Augmentationcited by 25
  23. 2024Data Augmentation of Contrastive Learning is Estimating Positive-incentive Noisecited by 25
  24. 2021Training Robust Graph Neural Networks with Topology Adaptive Edge Droppingcited by 20
  25. 2024Why Does Dropping Edges Usually Outperform Adding Edges in Graph Contrastive Learning?cited by 5
  26. 2023Perfect Alignment May be Poisonous to Graph Contrastive Learningcited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 13; 2026: 2.

15 papers.

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  1. 2025Mixture of Noise for Pre-Trained Model-Based Class-Incremental Learningcited by 14
  2. 2025Object-AVEdit: An Object-level Audio-Visual Editing Modelcited by 14
  3. 2025Integrating Reinforcement Learning with Visual Generative Models: Foundations and Advancescited by 12
  4. 2025Explore How to Inject Beneficial Noise in MLLMscited by 10
  5. 2025Rectified Noise: A Generative Model Using Positive-incentive Noisecited by 7
  6. 2025MR-UIE: Multi-Perspective Reasoning with Reinforcement Learning for Universal Information Extractioncited by 6
  7. 2025Laytrol: Preserving Pretrained Knowledge in Layout Control for Multimodal Diffusion Transformerscited by 6
  8. 2025SA2GFM: Enhancing Robust Graph Foundation Models with Structure-Aware Semantic Augmentationcited by 5
  9. 2025TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answeringcited by 3
  10. 2026Enhancing Neural Video Compression of Static Scenes with Positive-Incentive Noisecited by 3
  11. 2025GRPO-RM: Fine-Tuning Representation Models via GRPO-Driven Reinforcement Learningcited by 2
  12. 2025Real-Time Crowd Counting for Embedded Systems with Lightweight Architecturecited by 0
3 more
  1. 2025CoLM: Collaborative Large Models via A Client-Server Paradigmcited by 0
  2. 2025Multi-Intent Spoken Language Understanding: Methods, Trends, and Challengescited by 0
  3. 2026Noise-Robust Tiny Object Localization with Flowscited by 0