Awesome Graph Diffusion Models is a collection of graph generation works, including papers, codes and datasets.

[ICLR 2023] "Equivariant Hypergraph Diffusion Neural Operators" by Peihao Wang, Shenghao Yang, Yunyu Liu, Zhangyang Wang, Pan Li

[ICML 2023] Official implementation of "A randomized schur complement based graph augmentor"

[NeurIPS GLFrontiers 2023] DIGNN, Implicit Graph Neural Diffusion Models

Supervised graph diffusion and fusion.

Brain Graph Super-Resolution: how to generate high-resolution graphs from low-resolution graphs?

SM-NetFusion for supervised multi-topology network cross-diffusion.

Enhancing Spatiotemporal Supply Chain Forecasting Using Virtual Node-Augmented Graph Diffusion for Improved Fuel Efficiency

Latent Graph Diffusion 论文复现 | 图扩散生成 + 节点分类 | PyTorch + PyG