HOG-Diff: Higher-Order Guided Diffusion for Graph Generation

Official Implementations of HOG-Diff. In this work, we propose a novel Higher-order Guided Diffusion (HOG-Diff) model that follows a coarse-to-fine generation curriculum and is guided by higher-order information, enabling the progressive generation of authentic graphs with inherent topological structures.

🚀 We are continuously updating this repository and will release the complete code after the paper is accepted.

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Environment Setup

This code was tested with PyTorch 2.0.0, cuda 11.8 and torch_geometrics 2.6.1

1️⃣ Download anaconda/miniconda if needed

2️⃣ Create and Activate a Python Virtual Environment

conda create -n HoGD_py39 python=3.9
conda activate HoGD_py39

3️⃣ Install Dependencies

Use the provided requirements.txt file to install all dependencies:

pip install -r requirements.txt

4️⃣ Compile the ORCA Program (for Graph Generation Evaluation)

For evaluating generic graph generation tasks, compile the ORCA program by running the following command:

cd evaluation/orca 
g++ -O2 -std=c++11 -o orca orca.cpp

Running Experiments

To train and sample graphs using HOG-Diff, use the following commands:

CUDA_VISIBLE_DEVICES=0 python main.py --config config_name --mode train_ho
CUDA_VISIBLE_DEVICES=0 python main.py --config config_name --mode train_OU
CUDA_VISIBLE_DEVICES=0 python main.py --config config_name --mode sample

Replace config_name with the appropriate configuration file.

Citation

Please cite our work if you find our code/paper is useful to your work. :

🍀 Thank you for your interest in our work. 🍀

If you have any questions or encounter any issues while using our code, please don't hesitate to raise an issue or reach out to us directly.