Graph Generation with Diffusion Mixture

Official Code Repository for the paper Graph Generation with Diffusion Mixture (ICML 2024).

In this repository, we implement the Graph Diffusion Mixture (GruM).

Why GruM?

• Previous diffusion models cannot accurately model the graph structures as they learn to denoise at each step without considering the topology of the graphs to be generated.

• To fix such a myopic behavior of previous diffusion models, we propose a new graph generation framework that captures the graph structures by directly predicting the final graph of the diffusion process modeled by a mixture of endpoint-conditioned diffusion processes.

• Our method significantly outperforms previous graph diffusion models on the generation of diverse real and synthetic graphs, as well as on 2D/3D molecule generation tasks.

Code structure

We provide two separate projects of GruM for three types of graph generation tasks:

• General graph
• 2D molecule
• 3D molecule

Each projects consists of the following:

GruM_2D : Code for general graph generation / 2D molecule generation 

GruM_3D : Code for 3D molecule generation

We provide the details in README.md for each projects.

Dependencies

Create an environment with Python 3.9.15 and Pytorch 1.12.1. Use the following command to install the requirements:

pip install -r requirements.txt
conda install pyg -c pyg
conda install -c conda-forge graph-tool=2.45
conda install -c conda-forge rdkit=2022.03.2

Citation

If you found the provided code with our paper useful in your work, we kindly request that you cite our work.

@article{jo2024GruM,
  author    = {Jaehyeong Jo and
               Dongki Kim and
               Sung Ju Hwang},
  title     = {Graph Generation with Diffusion Mixture},
  journal   = {arXiv:2302.03596},
  year      = {2024},
  url       = {https://arxiv.org/abs/2302.03596}
}