mlsanityData to 7 Oct 2026

Diffusion-based Graph Generative Methods

arXiv 2401.15617 ↗First posted 28 Jan 2024cs.LG cs.AIcited by 2

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

Abstract (arXiv)

Being the most cutting-edge generative methods, diffusion methods have shown great advances in wide generation tasks. Among them, graph generation attracts significant research attention for its broad application in real life. In our survey, we systematically and comprehensively review on diffusion-based graph generative methods. We first make a review on three mainstream paradigms of diffusion methods, which are denoising diffusion probabilistic models, score-based genrative models, and stochastic differential equations. Then we further categorize and introduce the latest applications of diffusion models on graphs. In the end, we point out some limitations of current studies and future directions of future explorations. The summary of existing methods metioned in this survey is in https://github.com/zhejiangzhuque/Diffusion-based-Graph-Generative-Methods.

Key facts. Cited by 2 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 124 of its 188 references that are in the map. First posted on arXiv 28 Jan 2024.

Builds on

124 of 188 references.

Closest ancestors · 36

Same direction, newest first

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  24. 2020Permutation Invariant Graph Generation via Score-Based Generative Modeling2 Mar

Other references · 88

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  5. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
  6. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,005
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  8. 2019Generative Modeling by Estimating Gradients of the Data Distributioncited by 4,067
  9. 2017Neural Message Passing for Quantum Chemistrycited by 3,460
  10. 2014Conditional Generative Adversarial Netscited by 2,152
  11. 2016Density estimation using Real NVPcited by 2,029
  12. 2020Conformer: Convolution-augmented Transformer for Speech Recognitioncited by 1,838
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  6. 2021E(n) Equivariant Graph Neural Networkscited by 951
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  9. 2016NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysiscited by 683
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  12. 2017SchNet: A continuous-filter convolutional neural network for modeling quantum interactionscited by 569
  13. 2022Executing your Commands via Motion Diffusion in Latent Spacecited by 567
  14. 2020Jukebox: A Generative Model for Musiccited by 535
  15. 2021AI Choreographer: Music Conditioned 3D Dance Generation with AIST++cited by 479
  16. 2020Action2Motion: Conditioned Generation of 3D Human Motionscited by 438
  17. 2016Tutorial on Variational Autoencoderscited by 404
  18. 20203D-FRONT: 3D Furnished Rooms with layOuts and semaNTicscited by 375
  19. 2023Human Motion Diffusion as a Generative Priorcited by 354
  20. 2016The KIT Motion-Language Datasetcited by 334
  21. 2022DiffDock: Diffusion Steps, Twists, and Turns for Molecular Dockingcited by 307
  22. 2020GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generationcited by 301
  23. 2022EDGE: Editable Dance Generation From Musiccited by 284
  24. 2022Torsional Diffusion for Molecular Conformer Generationcited by 262
  25. 2021BABEL: Bodies, Action and Behavior with English Labelscited by 261
  26. 2020GEOM: Energy-annotated molecular conformations for property prediction and molecular generationcited by 238
  27. 2023Diffusion-based Generation, Optimization, and Planning in 3D Scenescited by 237
  28. 2022MoFusion: A Framework for Denoising-Diffusion-based Motion Synthesiscited by 218
  29. 2022FLAME: Free-form Language-based Motion Synthesis & Editingcited by 217
  30. 2023DIFUSCO: Graph-based Diffusion Solvers for Combinatorial Optimizationcited by 216
  31. 2022Listen, Denoise, Action! Audio-Driven Motion Synthesis with Diffusion Modelscited by 188
  32. 2020MoFlow: An Invertible Flow Model for Generating Molecular Graphscited by 183
  33. 2022Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pocketscited by 182
  34. 20233D Equivariant Diffusion for Target-Aware Molecule Generation and Affinity Predictioncited by 176
  35. 2021Learning Gradient Fields for Molecular Conformation Generationcited by 162
  36. 2023DiffuScene: Denoising Diffusion Models for Generative Indoor Scene Synthesiscited by 159
  37. 2022A 3D Generative Model for Structure-Based Drug Designcited by 148
  38. 2022Generating 3D Molecules for Target Protein Bindingcited by 104

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Citing papers per year: 2025: 2.

2 papers.

  1. 2025DiffNMR: Diffusion Models for Nuclear Magnetic Resonance Spectra Elucidationcited by 5
  2. 2025DMol: A Highly Efficient and Chemical Motif-Preserving Molecule Generation Platformcited by 1

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