mlsanityData to 14 Oct 2026

Deep Graph Generators: A Survey

arXiv 2012.15544 ↗First posted 31 Dec 2020cs.LG cs.AI cs.SIcited by 36

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Abstract (arXiv)

Deep generative models have achieved great success in areas such as image, speech, and natural language processing in the past few years. Thanks to the advances in graph-based deep learning, and in particular graph representation learning, deep graph generation methods have recently emerged with new applications ranging from discovering novel molecular structures to modeling social networks. This paper conducts a comprehensive survey on deep learning-based graph generation approaches and classifies them into five broad categories, namely, autoregressive, autoencoder-based, RL-based, adversarial, and flow-based graph generators, providing the readers a detailed description of the methods in each class. We also present publicly available source codes, commonly used datasets, and the most widely utilized evaluation metrics. Finally, we highlight the existing challenges and discuss future research directions.

Key facts. Cited by 36 papers in the map (data through 14 Oct 2026). First posted on arXiv 31 Dec 2020.

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Citing papers per year: 2021: 5; 2022: 13; 2023: 5; 2024: 8; 2025: 4; 2026: 1.

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  1. 2023Generative Diffusion Models on Graphs: Methods and Applicationscited by 81
  2. 2021DIG: A Turnkey Library for Diving into Graph Deep Learning Researchcited by 76
  3. 2022A Survey on Deep Graph Generation: Methods and Applicationscited by 69
  4. 2021Learn Locally, Correct Globally: A Distributed Algorithm for Training Graph Neural Networkscited by 24
  5. 2022Controllable Data Generation by Deep Learning: A Reviewcited by 20
  6. 2021Deconvolutional Networks on Graph Datacited by 16
  7. 2022Molecule Generation for Drug Design: a Graph Learning Perspectivecited by 13
  8. 2023SaGess: Sampling Graph Denoising Diffusion Model for Scalable Graph Generationcited by 13
  9. 2022G2GT: Retrosynthesis Prediction with Graph to Graph Attention Neural Network and Self-Trainingcited by 6
  10. 2022Graph Machine Learning for Design of High-Octane Fuelscited by 6
  11. 2022A Framework for Large Scale Synthetic Graph Dataset Generationcited by 6
  12. 2025Graph Diffusion that can Insert and Deletecited by 6
24 more
  1. 2021CCGG: A Deep Autoregressive Model for Class-Conditional Graph Generationcited by 4
  2. 2022A Survey on Temporal Graph Representation Learning and Generative Modelingcited by 4
  3. 2023MHG-GNN: Combination of Molecular Hypergraph Grammar with Graph Neural Networkcited by 4
  4. 2024An embedding-based distance for temporal graphscited by 4
  5. 2024A Review on Fragment-based De Novo 2D Molecule Generationcited by 4
  6. 2022An Unpooling Layer for Graph Generationcited by 3
  7. 2022Deception for Cyber Defence: Challenges and Opportunitiescited by 2
  8. 2022SCGG: A Deep Structure-Conditioned Graph Generative Modelcited by 2
  9. 2023Modeling and design of heterogeneous hierarchical bioinspired spider web structures using generative deep learning and additive manufacturingcited by 2
  10. 2024Rumor Detection on Social Media with Reinforcement Learning-based Key Propagation Graph Generatorcited by 2
  11. 2024Large Generative Graph Modelscited by 2
  12. 2025GDGB: A Benchmark for Generative Dynamic Text-Attributed Graph Learningcited by 2
  13. 2021Operator Autoencoders: Learning Physical Operations on Encoded Molecular Graphscited by 1
  14. 2022GraphTune: A Learning-based Graph Generative Model with Tunable Structural Featurescited by 1
  15. 2023SANGEA: Scalable and Attributed Network Generationcited by 1
  16. 2024Introducing Diminutive Causal Structure into Graph Representation Learningcited by 1
  17. 2024Towards the generation of hierarchical attack models from cybersecurity vulnerabilities using language modelscited by 1
  18. 2024Graph Community Augmentation with GMM-based Modeling in Latent Spacecited by 1
  19. 2025Graph VQ-Transformer (GVT): Fast and Accurate Molecular Generation via High-Fidelity Discrete Latentscited by 1
  20. 2022Gransformer: Transformer-based Graph Generationcited by 0
  21. 2022GrannGAN: Graph annotation generative adversarial networkscited by 0
  22. 2024Deep Generative Models for Subgraph Predictioncited by 0
  23. 2025Synthesizing Diverse Network Flow Datasets with Scalable Dynamic Multigraph Generationcited by 0
  24. 2026GraphK: Variable-Size Graph Generation with Efficient Edge Constructioncited by 0