# A Survey on Graph Diffusion Models: Generative AI in Science for Molecule, Protein and Material

arXiv: https://arxiv.org/abs/2304.01565 · HTML: https://mlsanity.com/paper/2304.01565/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- Placed in Flow matching, time series and science, one of 14 directions in Diffusion models.
- Builds on 105 of its 149 references that are in the map.
- First posted on arXiv 4 Apr 2023.

## Placed in

- Diffusion models / [Flow matching, time series and science](https://mlsanity.com/diffusion/flow-matching-time-series-and-science/)

## Builds on (most cited first, 30 of 105)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Generative Modeling by Estimating Gradients of the Data Distribution](https://mlsanity.com/paper/1907.05600/) (2019)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [Variational Inference with Normalizing Flows](https://mlsanity.com/paper/1505.05770/) (2015)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [Density estimation using Real NVP](https://mlsanity.com/paper/1605.08803/) (2016)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [Variational Graph Auto-Encoders](https://mlsanity.com/paper/1611.07308/) (2016)
- [Graph Convolutional Neural Networks for Web-Scale Recommender Systems](https://mlsanity.com/paper/1806.01973/) (2018)
- [Diffusion Models: A Comprehensive Survey of Methods and Applications](https://mlsanity.com/paper/2209.00796/) (2022)
- [Deep Graph Infomax](https://mlsanity.com/paper/1809.10341/) (2018)
- [Spectral Networks and Locally Connected Networks on Graphs](https://mlsanity.com/paper/1312.6203/) (2013)
- [MoleculeNet: A Benchmark for Molecular Machine Learning](https://mlsanity.com/paper/1703.00564/) (2017)
- [Improved Techniques for Training Score-Based Generative Models](https://mlsanity.com/paper/2006.09011/) (2020)
- [Revisiting Semi-Supervised Learning with Graph Embeddings](https://mlsanity.com/paper/1603.08861/) (2016)
- [E(n) Equivariant Graph Neural Networks](https://mlsanity.com/paper/2102.09844/) (2021)

## Built on by (most cited first, 30 of 35)

- [MLAgentBench: Evaluating Language Agents on Machine Learning Experimentation](https://mlsanity.com/paper/2310.03302/) (2023)
- [Position: Topological Deep Learning is the New Frontier for Relational Learning](https://mlsanity.com/paper/2402.08871/) (2024)
- [On the Design Fundamentals of Diffusion Models: A Survey](https://mlsanity.com/paper/2306.04542/) (2023)
- [Enhancing Deep Reinforcement Learning: A Tutorial on Generative Diffusion Models in Network Optimization](https://mlsanity.com/paper/2308.05384/) (2023)
- [A Comprehensive Survey on Data Augmentation](https://mlsanity.com/paper/2405.09591/) (2024)
- [A Survey on ChatGPT: AI-Generated Contents, Challenges, and Solutions](https://mlsanity.com/paper/2305.18339/) (2023)
- [Segment Anything Model (SAM) Meets Glass: Mirror and Transparent Objects Cannot Be Easily Detected](https://mlsanity.com/paper/2305.00278/) (2023)
- [A Survey of Generative AI for de novo Drug Design: New Frontiers in Molecule and Protein Generation](https://mlsanity.com/paper/2402.08703/) (2024)
- [SpectralDiff: A Generative Framework for Hyperspectral Image Classification with Diffusion Models](https://mlsanity.com/paper/2304.05961/) (2023)
- [Diffusion Models in textitDe Novo Drug Design](https://mlsanity.com/paper/2406.08511/) (2024)
- [Hyperbolic Geometric Latent Diffusion Model for Graph Generation](https://mlsanity.com/paper/2405.03188/) (2024)
- [DiffSG: A Generative Solver for Network Optimization with Diffusion Model](https://mlsanity.com/paper/2408.06701/) (2024)
- [Graph Diffusion Policy Optimization](https://mlsanity.com/paper/2402.16302/) (2024)
- [A Comprehensive Survey on Generative Diffusion Models for Structured Data](https://mlsanity.com/paper/2306.04139/) (2023)
- [A Survey of Generative AI for Intelligent Transportation Systems: Road Transportation Perspective](https://mlsanity.com/paper/2312.08248/) (2023)
- [VC dimension of Graph Neural Networks with Pfaffian activation functions](https://mlsanity.com/paper/2401.12362/) (2024)
- [Attack-SAM: Towards Attacking Segment Anything Model With Adversarial Examples](https://mlsanity.com/paper/2305.00866/) (2023)
- [Rao-Blackwell Gradient Estimators for Equivariant Denoising Diffusion](https://mlsanity.com/paper/2502.09890/) (2025)
- [A Literature Review of Literature Reviews in Pattern Analysis and Machine Intelligence](https://mlsanity.com/paper/2402.12928/) (2024)
- [Diffusion-based Graph Generative Methods](https://mlsanity.com/paper/2401.15617/) (2024)
- [Inference Attacks Against Graph Generative Diffusion Models](https://mlsanity.com/paper/2601.03701/) (2026)
- [On the importance of catalyst-adsorbate 3D interactions for relaxed energy predictions](https://mlsanity.com/paper/2310.06682/) (2023)
- [DMol: A Highly Efficient and Chemical Motif-Preserving Molecule Generation Platform](https://mlsanity.com/paper/2504.06312/) (2025)
- [Integrating Machine Learning into Belief-Desire-Intention Agents: Current Advances and Open Challenges](https://mlsanity.com/paper/2510.20641/) (2025)
- [Multi-objective Molecular Optimization for Opioid Use Disorder Treatment Using Generative Network Complex](https://mlsanity.com/paper/2306.07484/) (2023)
- [Combinatorial Complex Score-based Diffusion Modelling through Stochastic Differential Equations](https://mlsanity.com/paper/2406.04916/) (2024)
- [GUISE: Graph GaUssIan Shading watErmark](https://mlsanity.com/paper/2410.10178/) (2024)
- [Towards Automated Self-Supervised Learning for Truly Unsupervised Graph Anomaly Detection](https://mlsanity.com/paper/2501.14694/) (2025)
- [A Deep Generative Model for the Simulation of Discrete Karst Networks](https://mlsanity.com/paper/2506.09832/) (2025)
- [FAROS: Fair Graph Generation via Attribute Switching Mechanisms](https://mlsanity.com/paper/2507.03728/) (2025)

## Abstract (arXiv)

Diffusion models have become a new SOTA generative modeling method in various fields, for which there are multiple survey works that provide an overall survey. With the number of articles on diffusion models increasing exponentially in the past few years, there is an increasing need for surveys of diffusion models on specific fields. In this work, we are committed to conducting a survey on the graph diffusion models. Even though our focus is to cover the progress of diffusion models in graphs, we first briefly summarize how other generative modeling methods are used for graphs. After that, we introduce the mechanism of diffusion models in various forms, which facilitates the discussion on the graph diffusion models. The applications of graph diffusion models mainly fall into the category of AI-generated content (AIGC) in science, for which we mainly focus on how graph diffusion models are utilized for generating molecules and proteins but also cover other cases, including materials design. Moreover, we discuss the issue of evaluating diffusion models in the graph domain and the existing challenges.