# Deep Graph Generators: A Survey

arXiv: https://arxiv.org/abs/2012.15544 · HTML: https://mlsanity.com/paper/2012.15544/

## Key facts

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

## Built on by (most cited first, 30 of 36)

- [Generative Diffusion Models on Graphs: Methods and Applications](https://mlsanity.com/paper/2302.02591/) (2023)
- [DIG: A Turnkey Library for Diving into Graph Deep Learning Research](https://mlsanity.com/paper/2103.12608/) (2021)
- [A Survey on Deep Graph Generation: Methods and Applications](https://mlsanity.com/paper/2203.06714/) (2022)
- [Learn Locally, Correct Globally: A Distributed Algorithm for Training Graph Neural Networks](https://mlsanity.com/paper/2111.08202/) (2021)
- [Controllable Data Generation by Deep Learning: A Review](https://mlsanity.com/paper/2207.09542/) (2022)
- [Deconvolutional Networks on Graph Data](https://mlsanity.com/paper/2110.15528/) (2021)
- [Molecule Generation for Drug Design: a Graph Learning Perspective](https://mlsanity.com/paper/2202.09212/) (2022)
- [SaGess: Sampling Graph Denoising Diffusion Model for Scalable Graph Generation](https://mlsanity.com/paper/2306.16827/) (2023)
- [G2GT: Retrosynthesis Prediction with Graph to Graph Attention Neural Network and Self-Training](https://mlsanity.com/paper/2204.08608/) (2022)
- [Graph Machine Learning for Design of High-Octane Fuels](https://mlsanity.com/paper/2206.00619/) (2022)
- [A Framework for Large Scale Synthetic Graph Dataset Generation](https://mlsanity.com/paper/2210.01944/) (2022)
- [Graph Diffusion that can Insert and Delete](https://mlsanity.com/paper/2506.15725/) (2025)
- [CCGG: A Deep Autoregressive Model for Class-Conditional Graph Generation](https://mlsanity.com/paper/2110.03800/) (2021)
- [A Survey on Temporal Graph Representation Learning and Generative Modeling](https://mlsanity.com/paper/2208.12126/) (2022)
- [MHG-GNN: Combination of Molecular Hypergraph Grammar with Graph Neural Network](https://mlsanity.com/paper/2309.16374/) (2023)
- [An embedding-based distance for temporal graphs](https://mlsanity.com/paper/2401.12843/) (2024)
- [A Review on Fragment-based De Novo 2D Molecule Generation](https://mlsanity.com/paper/2405.05293/) (2024)
- [An Unpooling Layer for Graph Generation](https://mlsanity.com/paper/2206.01874/) (2022)
- [Deception for Cyber Defence: Challenges and Opportunities](https://mlsanity.com/paper/2208.07127/) (2022)
- [SCGG: A Deep Structure-Conditioned Graph Generative Model](https://mlsanity.com/paper/2209.09681/) (2022)
- [Modeling and design of heterogeneous hierarchical bioinspired spider web structures using generative deep learning and additive manufacturing](https://mlsanity.com/paper/2304.05137/) (2023)
- [Rumor Detection on Social Media with Reinforcement Learning-based Key Propagation Graph Generator](https://mlsanity.com/paper/2405.13094/) (2024)
- [Large Generative Graph Models](https://mlsanity.com/paper/2406.05109/) (2024)
- [GDGB: A Benchmark for Generative Dynamic Text-Attributed Graph Learning](https://mlsanity.com/paper/2507.03267/) (2025)
- [Operator Autoencoders: Learning Physical Operations on Encoded Molecular Graphs](https://mlsanity.com/paper/2105.12295/) (2021)
- [GraphTune: A Learning-based Graph Generative Model with Tunable Structural Features](https://mlsanity.com/paper/2201.11494/) (2022)
- [SANGEA: Scalable and Attributed Network Generation](https://mlsanity.com/paper/2309.15648/) (2023)
- [Introducing Diminutive Causal Structure into Graph Representation Learning](https://mlsanity.com/paper/2406.08709/) (2024)
- [Towards the generation of hierarchical attack models from cybersecurity vulnerabilities using language models](https://mlsanity.com/paper/2410.05351/) (2024)
- [Graph Community Augmentation with GMM-based Modeling in Latent Space](https://mlsanity.com/paper/2412.01163/) (2024)

## 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.