mlsanityData to 7 Oct 2026

Generative Diffusion Models on Graphs: Methods and Applications

arXiv 2302.02591 ↗First posted 6 Feb 2023cs.LG cs.AI cs.SIcited by 79

On the map: Diffusion models → Flow matching, time series and science

Abstract (arXiv)

Diffusion models, as a novel generative paradigm, have achieved remarkable success in various image generation tasks such as image inpainting, image-to-text translation, and video generation. Graph generation is a crucial computational task on graphs with numerous real-world applications. It aims to learn the distribution of given graphs and then generate new graphs. Given the great success of diffusion models in image generation, increasing efforts have been made to leverage these techniques to advance graph generation in recent years. In this paper, we first provide a comprehensive overview of generative diffusion models on graphs, In particular, we review representative algorithms for three variants of graph diffusion models, i.e., Score Matching with Langevin Dynamics (SMLD), Denoising Diffusion Probabilistic Model (DDPM), and Score-based Generative Model (SGM). Then, we summarize the major applications of generative diffusion models on graphs with a specific focus on molecule and protein modeling. Finally, we discuss promising directions in generative diffusion models on graph-structured data. For this survey, we also created a GitHub project website by collecting the supporting resources for generative diffusion models on graphs, at the link: https://github.com/ChengyiLIU-cs/Generative-Diffusion-Models-on-Graphs

Key facts. Cited by 79 papers in the map (data through 7 Oct 2026). Placed in Flow matching, time series and science, one of 14 directions in Diffusion models. Builds on 77 of its 122 references that are in the map. First posted on arXiv 6 Feb 2023.

Builds on

77 of 122 references.

Closest ancestors · 11

Same direction, newest first

  1. 2022Equivariant 3D-Conditional Diffusion Models for Molecular Linker Design11 Oct
  2. 2022Diffusion Models for Graphs Benefit From Discrete State Spaces4 Oct
  3. 2022Protein structure generation via folding diffusion30 Sep
  4. 2022DiGress: Discrete Denoising diffusion for graph generation29 Sep
  5. 2022MDM: Molecular Diffusion Model for 3D Molecule Generation13 Sep
  6. 2022Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problem8 Jun
  7. 2022Protein Structure and Sequence Generation with Equivariant Denoising Diffusion Probabilistic Models26 May
  8. 2022Equivariant Diffusion for Molecule Generation in 3D31 Mar
  9. 2022GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generation6 Mar
  10. 2022Score-based Generative Modeling of Graphs via the System of Stochastic Differential Equations5 Feb
  11. 2020Permutation Invariant Graph Generation via Score-Based Generative Modeling2 Mar

Other references · 66

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  4. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  5. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
  6. 2018How Powerful are Graph Neural Networks?cited by 4,355
  7. 2021Zero-Shot Text-to-Image Generationcited by 4,276
  8. 2019Generative Modeling by Estimating Gradients of the Data Distributioncited by 4,067
  9. 2021GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Modelscited by 3,595
  10. 2021Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processingcited by 2,222
  11. 2014Conditional Generative Adversarial Netscited by 2,152
  12. 2021Structured Denoising Diffusion Models in Discrete State-Spacescited by 1,665
38 more
  1. 2016Variational Graph Auto-Encoderscited by 1,373
  2. 2022Diffusion Models: A Comprehensive Survey of Methods and Applicationscited by 1,107
  3. 2022Diffusion Models in Vision: A Surveycited by 1,008
  4. 2022Diffusion-LM Improves Controllable Text Generationcited by 1,006
  5. 2019Graph Neural Networks for Social Recommendationcited by 711
  6. 2020A Generalization of Transformer Networks to Graphscited by 653
  7. 2020Graph Neural Networks in Recommender Systems: A Surveycited by 493
  8. 2017Representation Learning on Graphs: Methods and Applicationscited by 456
  9. 2018Deep Learning on Graphs: A Surveycited by 393
  10. 2018MolGAN: An implicit generative model for small molecular graphscited by 392
  11. 2020Explainability in Graph Neural Networks: A Taxonomic Surveycited by 383
  12. 2018GraphVAE: Towards Generation of Small Graphs Using Variational Autoencoderscited by 375
  13. 2022DiffDock: Diffusion Steps, Twists, and Turns for Molecular Dockingcited by 307
  14. 2017Molecular De Novo Design through Deep Reinforcement Learningcited by 264
  15. 2022Torsional Diffusion for Molecular Conformer Generationcited by 262
  16. 2021Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Modelscited by 251
  17. 2020Equivariant Flows: Exact Likelihood Generative Learning for Symmetric Densitiescited by 248
  18. 2019Efficient Graph Generation with Graph Recurrent Attention Networkscited by 231
  19. 2021A Comprehensive Survey on Graph Anomaly Detection with Deep Learningcited by 230
  20. 2020A Survey on Causal Inferencecited by 202
  21. 2018Optimization of Molecules via Deep Reinforcement Learningcited by 199
  22. 2018Constrained Graph Variational Autoencoders for Molecule Designcited by 182
  23. 2021GraphDF: A Discrete Flow Model for Molecular Graph Generationcited by 177
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  28. 2021Accurate Learning of Graph Representations with Graph Multiset Poolingcited by 120
  29. 2022A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainabilitycited by 118
  30. 2021GeoMol: Torsional Geometric Generation of Molecular 3D Conformer Ensemblescited by 106
  31. 2022Diffusion-based Molecule Generation with Informative Prior Bridgescited by 104
  32. 2019Graph Normalizing Flowscited by 103
  33. 2022Exploring Chemical Space with Score-based Out-of-distribution Generationcited by 95
  34. 2020A Systematic Survey on Deep Generative Models for Graph Generationcited by 94
  35. 2021Trustworthy AI: A Computational Perspectivecited by 93
  36. 2022Equivariant Energy-Guided SDE for Inverse Molecular Designcited by 72
  37. 2022A Survey on Deep Graph Generation: Methods and Applicationscited by 69
  38. 2019Mol-CycleGAN - a generative model for molecular optimizationcited by 66

50 most cited of 66 All 66 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 12; 2024: 24; 2025: 23; 2026: 20.

79 papers.

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  1. 2023Recommender Systems in the Era of Large Language Models (LLMs)cited by 188
  2. 2025The Diffusion Dualitycited by 124
  3. 2023Empowering Molecule Discovery for Molecule-Caption Translation with Large Language Models: A ChatGPT Perspectivecited by 91
  4. 2024Discrete-state Continuous-time Diffusion for Graph Generationcited by 73
  5. 2023On the Design Fundamentals of Diffusion Models: A Surveycited by 59
  6. 2023Enhancing Deep Reinforcement Learning: A Tutorial on Generative Diffusion Models in Network Optimizationcited by 56
  7. 2023Pyramid Diffusion for Fine 3D Large Scene Generationcited by 44
  8. 2024A Comprehensive Survey on Data Augmentationcited by 38
  9. 2023Fast Graph Condensation with Structure-based Neural Tangent Kernelcited by 36
  10. 2025Esoteric Language Models: A Family of Any-Order Diffusion LLMscited by 33
  11. 2024Diffusion Models in textitDe Novo Drug Designcited by 26
  12. 2023NAP: Neural 3D Articulation Priorcited by 25
38 more
  1. 2024Pard: Permutation-Invariant Autoregressive Diffusion for Graph Generationcited by 25
  2. 2023A Survey of Diffusion Based Image Generation Models: Issues and Their Solutionscited by 23
  3. 2024Score-based Generative Diffusion Models for Social Recommendationscited by 23
  4. 2023A Deep Instance Generative Framework for MILP Solvers Under Limited Data Availabilitycited by 21
  5. 2025Absorb and Converge: Provable Convergence Guarantee for Absorbing Discrete Diffusion Modelscited by 20
  6. 2025Discrete Diffusion Models: Novel Analysis and New Sampler Guaranteescited by 20
  7. 2024EditRoom: LLM-parameterized Graph Diffusion for Composable 3D Room Layout Editingcited by 14
  8. 2026The Diffusion Duality, Chapter II: Psi-Samplerscited by 12
  9. 2025Diffusion and Flow Matching Models for Tabular Data: A Surveycited by 8
  10. 2026Sharp Convergence Rates for Masked Diffusion Modelscited by 8
  11. 2024Machine Learning Insides OptVerse AI Solver: Design Principles and Applicationscited by 7
  12. 2025Bures-Wasserstein Flow Matching for Graph Generationcited by 7
  13. 2025Graph-Aware Diffusion for Signal Generationcited by 7
  14. 2024Reviving Life on the Edge: Joint Score-Based Graph Generation of Rich Edge Attributescited by 6
  15. 2025Computational Protein Science in the Era of Large Language Models (LLMs)cited by 6
  16. 2024SpecSTG: A Fast Spectral Diffusion Framework for Probabilistic Spatio-Temporal Traffic Forecastingcited by 5
  17. 2024GDDA: Semantic OOD Detection on Graphs under Covariate Shift via Score-Based Diffusion Modelscited by 5
  18. 2025GLProtein: Global-and-Local Structure Aware Protein Representation Learningcited by 5
  19. 2024ToDA: Target-oriented Diffusion Attacker against Recommendation Systemcited by 4
  20. 2024Nonlinear Sheaf Diffusion in Graph Neural Networkscited by 4
  21. 2025Topological Schr"odinger Bridge Matchingcited by 4
  22. 2025Learning Individual Behavior in Agent-Based Models with Graph Diffusion Networkscited by 4
  23. 2025Backdoor Attacks on Discrete Graph Diffusion Modelscited by 3
  24. 2026Enhancing Molecular Property Predictions by Learning from Bond Modelling and Interactionscited by 3
  25. 2023Modeling and design of heterogeneous hierarchical bioinspired spider web structures using generative deep learning and additive manufacturingcited by 2
  26. 2024Diffusion-based Graph Generative Methodscited by 2
  27. 2025Improving Discriminator Guidance in Diffusion Modelscited by 2
  28. 2025Generative Models for Synthetic Data: Transforming Data Mining in the GenAI Eracited by 2
  29. 2026Inference Attacks Against Graph Generative Diffusion Modelscited by 2
  30. 2024Enhanced Droplet Analysis Using Generative Adversarial Networkscited by 1
  31. 2024On Diffusion Process in SE(3)-invariant Spacecited by 1
  32. 2024Distribution Backtracking Builds A Faster Convergence Trajectory for Diffusion Distillationcited by 1
  33. 2024Molecular Dynamics and Machine Learning Unlock Possibilities in Beauty Design -- A Perspectivecited by 1
  34. 2025Probabilistic Graph Circuits: Deep Generative Models for Tractable Probabilistic Inference over Graphscited by 1
  35. 2025DMol: A Highly Efficient and Chemical Motif-Preserving Molecule Generation Platformcited by 1
  36. 2025Transport-Coupled Bayesian Flows for Molecular Graph Generationcited by 1
  37. 2026When Are Two Scores Better Than One? Investigating Ensembles of Diffusion Modelscited by 1
  38. 2026Variational Bayesian Flow Network for Graph Generationcited by 1

50 most cited of 79 All 79 (JSON)

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Close in wording, no citation either way

  1. 2023A Survey on Graph Diffusion Models: Generative AI in Science for Molecule, Protein and Material
  2. 2023Autoregressive Diffusion Model for Graph Generation
  3. 2025Graph Representation Learning with Diffusion Generative Models
  4. 2023Graph Generation with Diffusion Mixture
  5. 2024Advancing Graph Generation through Beta Diffusion
  6. 2023Efficient and Degree-Guided Graph Generation via Discrete Diffusion Modeling
  7. 2025Diffusion-Free Graph Generation with Next-Scale Prediction
  8. 2024From Graph Diffusion to Graph Classification