The code is tested under a Linux desktop with torch 2.0.0 and Python 3.8.10.

• Tested OS: Linux
• Python >= 3.8
• PyTorch == 2.0.0
• torch_geometric == 1.7.2

1. Install PyTorch 2.0.0 with the correct CUDA version.
2. Use the pip install -r requirements. txt command to install all of the Python modules and packages used in this project.

Process data:

python process_data.py

Pretrain to get KG embeddings:

bash pretrain.sh

Train diffusion model:

bash train.sh

Generate urban flow:

bash sample.sh

Evaluate generated flow:

python evaluate.py

(The default dataset is Florida, you can modify the dataset in pretrain.sh, train.sh, sample.sh, and evaluate.py)

• Hierarchical Knowledge Graph Learning Enabled Socioeconomic Indicator Prediction in Location-Based Social Network (WWW '23)

The implemention is based on DDPM.

If you found this library useful in your research, please consider citing:

@article{zhou2023towards,
  title={Towards Generative Modeling of Urban Flow through Knowledge-enhanced Denoising Diffusion},
  author={Zhou, Zhilun and Ding, Jingtao and Liu, Yu and Jin, Depeng and Li, Yong},
  journal={arXiv preprint arXiv:2309.10547},
  year={2023}
}