A curated list of diffusion-based (score-based) graph generative models
- Sparse Training of Discrete Diffusion Models for Graph Generation (arXiv, 2023) [paper] [code]
- GraphMaker: Can Diffusion Models Generate Large Attributed Graphs? (arXiv, 2023) [paper] [code]
- Plug-and-Play Controllable Graph Generation with Diffusion Models (ICML SPIGM Workshop, 2023) [paper]
- Autoregressive Diffusion Model for Graph Generation (arXiv, 2023) [paper]
- SwinGNN: Rethinking Permutation Invariance in Diffusion Models for Graph Generation (arXiv, 2023) [paper] [code]
- Efficient and Degree-Guided Graph Generation via Discrete Diffusion Modeling (ICML, 2023) [paper] [code]
- GraphGUIDE: interpretable and controllable conditional graph generation with discrete Bernoulli diffusion (arXiv, 2023) [paper] [code]
- Graph Generation with Destination-Driven Diffusion Mixture (arXiv, 2023) [paper]
- GraphGDP: Generative Diffusion Processes for Permutation Invariant Graph Generation (IEEE ICDM, 2022) [paper] [code]
- NVDiff: Graph Generation through the Diffusion of Node Vectors (arXiv, 2022) [paper]
- Exploring the Design Space of Generative Diffusion Processes for Sparse Graphs (NeurIPS Workshop on Score-Based Methods, 2022) [paper]
- Diffusion Models for Graphs Benefit From Discrete State Spaces (LoG, 2022) [paper] [code]
- DiGress: Discrete Denoising diffusion for graph generation (ICLR, 2023) [paper] [code]
- Score-based Generative Modeling of Graphs via the System of Stochastic Differential Equations (ICML, 2022) [paper] [code]