This is the summation of all the methods, datasets, and other survey mentioned in our survey 'Graph Diffusion Models: A Comprehensive Survey of Methods and Applications' 🔥. Any problems, please contact shouyuntao@stu.xjtu.edu.cn. Any other interesting papers or codes are welcome. If you find this repository useful to your research or work, it is really appreciated to star this repository ❤️.
Methods | Paper | Code | Methods | Paper | Code |
---|---|---|---|---|---|
DiGress (ICLR-23) | [paper] | [code] | MiDi (ICLR-23) | [paper] | [code] |
CDGS (NeurIPS-22) | [paper] | [code] | GCDM (ICLR-23) | [paper] | [code] |
EDM (ICML-22) | [paper] | [code] | Wu et al. (NeurIPS-22) | [paper] | - |
MDM (AAAI-23) | [paper] | [code] | DiffLinker | [paper] | [code] |
JODO | [paper] | [code] | SILVR | [paper] | - |
HierDiff (ICML-23) | [paper] | [code] | - | - | - |
BIMODAL | [paper] | [code] | RationaleRL (ICML20) | [paper] | [code] |
GEOLDM (ICML-23) | [paper] | [code] | MGM | [paper] | - |
LFM AISTATS-20 | [paper] | [code] | RetMol | [paper] | [code] |
MolGPT | [paper] | - | Bridge (NeurIPS-2022) | [paper] | - |
Bresson et al. | [paper] | - | FLAG (ICLR-23) | [paper] | [code] |
LIMO | [paper] | [code] | D3FG NeurIPS-24 | [paper] | [code] |
Methods | Paper | Code | Methods | Paper | Code |
---|---|---|---|---|---|
ConfGF (ICML-21) | [paper] | [code] | DGSM (NeurIPS-21) | [paper] | - |
GeoDiff (ICLR-22) | [paper] | [code] | ColfNet (ICML-22) | [paper] | - |
Torsion Diffusion (NeurIPS-22) | [paper] | [code] | DiffMD (AAAI-23) | [paper] | - |
RINGER | [paper] | - | - | - | - |
Methods | Paper | Code | Methods | Paper | Code |
---|---|---|---|---|---|
DiffBP | [paper] | - | DiffSBDD | [paper] | - |
TargetDiff (ICLR-23) | [paper] | - | PMDM | [paper] | - |
D3FG | [paper] | - | - | - | - |
Methods | Paper | Code |
---|---|---|
DIFFDOCK (NeurIPS-22) | [paper] | - |
EDM-Dock (JCIM) | [paper] | [code] |
DPL | [paper] | [code] |
NeuralPLexer | [paper] | - |
E3BIND (ICLR-23) | [paper] | - |
Methods | Paper | Code | Methods | Paper | Code |
---|---|---|---|---|---|
DiffAb (NeurIPS-22) | [paper] | - | Anand | [paper] | - |
PROTSEED (ICLR-23) | [paper] | - | ProteinSGM | [paper] | [code] |
SMCDiff (ICLR-23) | [paper] | [code] | GraDe-IF | [paper] | - |
EigenFold | [paper] | [code] | - | - | - |
CGM (NeurIPS-19) | [paper] | [code] | SeqDesign | [paper] | [code] |
Fold2Seq (ICML-21) | [paper] | [code] | EvoDiff | [paper] | - |
GVP (ICLR-21) | [paper] | [code] | ESM (NeurIPS-21) | [paper] | - |
Methods | Paper | Code | Homepage | Methods | Paper | Code | Homepage |
---|---|---|---|---|---|---|---|
MotionDiffuse | [paper] | [code] | [homepage] | Modiff | [paper] | - | - |
Ren et al. (ICASSP-23) | [paper] | - | - | FLAME (AAAI-23) | [paper] | - | - |
MoFusion (CVPR-23) | [paper] | - | [homepage] | MDM (ICLR-23) | [paper] | [code] | [homepage] |
MLD (CVPR-23) | [paper] | [code] | [homepage] | PriorMDM | [paper] | [code] | [homepage] |
Alexanderson et al. (ACM Trans. Graph.) | [paper] | - | - | EDGE (CVPR-23) | [paper] | [code] | [homepage] |
SceneDiffuser | [paper] | [code] | [homepage] | MoDi (CVPR-23) | [paper] | [code] | [homepage] |
BiGraphDiff | [paper] | - | - | DiffuPose | [paper] | - | - |
Methods | Paper | Code | Homepage |
---|---|---|---|
Ahn et al. (ICRA-23) | [paper] | [code] | [homepage] |
HumanMAC | [paper] | [code] | [homepage] |
TCD (ICRA-23) | [paper] | [code] | - |
DiffMotion | [paper] | - | - |
Methods | Paper | Code | Homepage | Methods | Paper | Code | Homepage |
---|---|---|---|---|---|---|---|
EDP-GNN | [paper] | - | - | GSDM | [paper] | - | - |
NVDiff | [paper] | - | - | DPM-GSP | [paper] | - | - |
DiffSTG | [paper] | - | - | DIFUSCO | [paper] | - | - |
GraphGDP | [paper] | [code] | - | HouseDiffusion (CVPR-23) | [paper] | [code] | [homepage] |
NAP | [paper] | - | [homepage] | EDGE | [paper] | - | - |
DruM | [paper] | - | - | DDM | [Paper] | - | - |
DiffusionNAG | [paper] | - | - | TSDiff | [paper] | - | - |
GraphArm | [paper] | - | - | HGDM | [paper] | [code] | - |
Lee et al. | [paper] | - | - | SaGess | [paper] | - | - |
SLD | [paepr] | - | - | Diff-POI | [paper] | - | - |
Brain Diffuser | [paper] | - | - | Lu et al. | [paper] | - | - |
Dataset | Dimensionality | Category | No.of Graphs (G) | No. of Nodes (N) |
---|---|---|---|---|
Community-small | 2D | Social | 100 | 11 < N < 20 |
Ego-small | 2D | Social | 200 | 3 < N < 18 |
Grid | 2D | Grid | 100 | N <= 400 |
QM9 | 3D | Bioinformatics/Molecular | 130,831 | 3 < N < 29 |
ZINC250K | 3D | Bioinformatics/Molecular | 249,456 | 6 < N < 38 |
Enzymes | 3D | Bioinformatics/Protein | 600 | 9 < N < 125 |
SBM-27 | 2D | Social | 200 | 24 < N < 27 |
Planar-60 | 2D | Social | 200 | N = 60 |
AIDS | 2D | Bioinformatics/Molecular | 2000 | - |
Synthie | 2D | Social | 300 | N = 100 |
Proteins | 3D | Bioinformatics/Protein | 1113 | N = 39.1 |
Methods | Paper | Source | Methods | Paper | Source |
---|---|---|---|---|---|
Zinc | [paper] | [source] | GEOM-QM9 | [paper] | [source] |
GEOM-Drugs | [paper] | [source] | CrossDocked2020 | [paper] | [source] |
BioLiP | [paper] | [source] | PDBBind | [paper] | [source] |
SAbDab | [paper] | [source] | - | - | - |
Methods | Paper | Source | Methods | Paper | Source |
---|---|---|---|---|---|
Human3.6M | [paper] | [source] | HumanEva-I | [paper] | [source] |
HumanAct12 | [paper] | [source] | HumanML3D | [paper] | [source] |
KIT | [paper] | [source] | BABEL | [paper] | [source] |
UESTC | [paper] | [source] | 3DPW | [paper] | [source] |
NTU RGB+D | [paper] | [source] | AIST++ | [paper] | [source] |
TSG | [paper] | [source] | ZeroEGGS | [paper] | [source] |
Paper | Url | Source |
---|---|---|
Diffusion-based Graph Generative Methods | [paper] | [source] |
Diffusion Models: A Comprehensive Survey of Methods and Applications | [paper] | [source] |
A Survey on Generative Diffusion Model | [paper] | [source] |
Generative Diffusion Models on Graphs: Methods and Applications | [paper] | - |
A Survey on Graph Diffusion Models: Generative AI in Science for Molecule, Protein and Material | [paper] | - |
Graph-based Molecular Representation Learning | [paper] | - |
Generative Models as an Emerging Paradigm in the Chemical Sciences | [paper] | - |
A Survey on Deep Graph Generation: Methods and Applications (LoG-22) | [paper] | - |
A Survey on Temporal Graph Representation Learning and Generative Modeling | [paper] | - |
Human motion modeling with deep learning: A survey | [paper] | - |
MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Design | [paper] | - |
Thanks to Diffusion-based-Graph-Generative-Methods.