mlsanityData to 14 Oct 2026

NetGAN: Generating Graphs via Random Walks

arXiv 1803.00816 ↗First posted 2 Mar 2018stat.ML cs.LG cs.SIcited by 131

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Abstract (arXiv)

We propose NetGAN - the first implicit generative model for graphs able to mimic real-world networks. We pose the problem of graph generation as learning the distribution of biased random walks over the input graph. The proposed model is based on a stochastic neural network that generates discrete output samples and is trained using the Wasserstein GAN objective. NetGAN is able to produce graphs that exhibit well-known network patterns without explicitly specifying them in the model definition. At the same time, our model exhibits strong generalization properties, as highlighted by its competitive link prediction performance, despite not being trained specifically for this task. Being the first approach to combine both of these desirable properties, NetGAN opens exciting avenues for further research.

Key facts. Cited by 131 papers in the map (data through 14 Oct 2026). First posted on arXiv 2 Mar 2018.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 22; 2022: 30; 2023: 29; 2024: 19; 2025: 19; 2026: 12.

131 papers.

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  1. 2021Graph Learning: A Surveycited by 141
  2. 2022Data Augmentation for Deep Graph Learning: A Surveycited by 133
  3. 2021Deep Generative Models in Engineering Design: A Reviewcited by 105
  4. 2022SPECTRE: Spectral Conditioning Helps to Overcome the Expressivity Limits of One-shot Graph Generatorscited by 99
  5. 2022Revisiting Over-smoothing and Over-squashing Using Ollivier-Ricci Curvaturecited by 98
  6. 2023Efficient and Degree-Guided Graph Generation via Discrete Diffusion Modelingcited by 80
  7. 2022A Survey on Deep Graph Generation: Methods and Applicationscited by 69
  8. 2021Self-Contrastive Learning with Hard Negative Sampling for Self-supervised Point Cloud Learningcited by 52
  9. 2021Learning Theory Can (Sometimes) Explain Generalisation in Graph Neural Networkscited by 51
  10. 2021Constructions in combinatorics via neural networkscited by 48
  11. 2022GraphGDP: Generative Diffusion Processes for Permutation Invariant Graph Generationcited by 43
  12. 2023A Survey on Graph Diffusion Models: Generative AI in Science for Molecule, Protein and Materialcited by 35
38 more
  1. 2022Bringing Your Own View: Graph Contrastive Learning without Prefabricated Data Augmentationscited by 28
  2. 2021Unconditional Scene Graph Generationcited by 22
  3. 2021Hybrid Quantum-Classical Graph Convolutional Networkcited by 21
  4. 2022TIGGER: Scalable Generative Modelling for Temporal Interaction Graphscited by 18
  5. 2023Continual Graph Learning: A Surveycited by 18
  6. 2023GraphMaker: Can Diffusion Models Generate Large Attributed Graphs?cited by 18
  7. 2021On the Power of Edge Independent Graph Modelscited by 16
  8. 2022Interpretable Molecular Graph Generation via Monotonic Constraintscited by 15
  9. 2022Deep Graph Learning for Anomalous Citation Detectioncited by 14
  10. 2023SaGess: Sampling Graph Denoising Diffusion Model for Scalable Graph Generationcited by 13
  11. 2025Flatten Graphs as Sequences: Transformers are Scalable Graph Generatorscited by 13
  12. 2023FairGen: Towards Fair Graph Generationcited by 12
  13. 2023A Comprehensive Review of Community Detection in Graphscited by 12
  14. 2021Partition and Code: learning how to compress graphscited by 10
  15. 2023Complexity-aware Large Scale Origin-Destination Network Generation via Diffusion Modelcited by 9
  16. 2025Joint Relational Database Generation via Graph-Conditional Diffusion Modelscited by 9
  17. 2021Graph Representation Learning for Road Type Classificationcited by 8
  18. 2022Analysis of Convolutions, Non-linearity and Depth in Graph Neural Networks using Neural Tangent Kernelcited by 8
  19. 2022Micro and Macro Level Graph Modeling for Graph Variational Auto-Encoderscited by 8
  20. 2023Origin-Destination Network Generation via Gravity-Guided GANcited by 8
  21. 2023Graph Contrastive Learning with Generative Adversarial Networkcited by 8
  22. 2022Data-Free Adversarial Knowledge Distillation for Graph Neural Networkscited by 7
  23. 2023Let There Be Order: Rethinking Ordering in Autoregressive Graph Generationcited by 7
  24. 2022A Framework for Large Scale Synthetic Graph Dataset Generationcited by 6
  25. 2023State of the Art and Potentialities of Graph-level Learningcited by 6
  26. 2023HiGen: Hierarchical Graph Generative Networkscited by 6
  27. 2024FairWire: Fair Graph Generationcited by 6
  28. 2021Adversarial Graph Disentanglementcited by 5
  29. 2021A Study of Joint Graph Inference and Forecastingcited by 5
  30. 2022A Graph and Attentive Multi-Path Convolutional Network for Traffic Predictioncited by 5
  31. 2022Realistic Synthetic Social Networks with Graph Neural Networkscited by 5
  32. 2024Efficient Dynamic Attributed Graph Generationcited by 5
  33. 2025Generative AI in Depth: A Survey of Recent Advances, Model Variants, and Real-World Applicationscited by 5
  34. 2021CCGG: A Deep Autoregressive Model for Class-Conditional Graph Generationcited by 4
  35. 2022Node Copying: A Random Graph Model for Effective Graph Samplingcited by 4
  36. 2022A Survey on Temporal Graph Representation Learning and Generative Modelingcited by 4
  37. 2022Predicting Drug-Drug Interactions using Deep Generative Models on Graphscited by 4
  38. 2022EDoG: Adversarial Edge Detection For Graph Neural Networkscited by 4

50 most cited of 131 All 131 (JSON)