This is for our CIKM2022 paper《Heterogeneous Hypergraph Neural Network for Friend Recommendation with Human Mobility》
Thank you for your interest in the content of our research.
Before to execute HHGNN, it is necessary to install the following packages:
pip install dgl
pip install torch
pip install scikit-learn
pip install torch-scatter
- numpy ==1.13.1
- torch ==1.7.1
- scikit-learn==1.0.2
- dgl == 0.7.2
- torch-scatter == 2.0.7
【September 11, 2024】The modified code now supports the latest versions of torch and DGL.
- --Please run train.py to train the HHGNN in NYC city.
- Due to the huge number of hyperedges, our algorithm needs to occupy a large storage space of GPU. It probably need at least 18G GPU memory for NYC city.
- You can reduce the GPU memory usage by reducing the number of multi-heads or the dimension of the output layer. More adjustable hyperparameters in the config.py file
【March 3, 2025】 Please refer to the code implementation in H3GNN to learn more about processing heterogeneous hypergraph data. We provide an updated version of the code that can complete the data preprocessing for a single city within 10 minutes.
We welcome your suggestions and encouragement to help us keep improving. And we urge everyone to contribute to the development of the community together.
We will continue to update and adjust and optimize our algorithm to make it more acceptable to everyone.
If you find this work helpful, please consider citing our paper:
@inproceedings{li2022hhgnn,
author = {Li, Yongkang and Fan, Zipei and Zhang, Jixiao and Shi, Dengheng and Xu, Tianqi and Yin, Du and Deng, Jinliang and Song, Xuan},
title = {Heterogeneous Hypergraph Neural Network for Friend Recommendation with Human Mobility},
year = {2022},
isbn = {9781450392365},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3511808.3557609},
doi = {10.1145/3511808.3557609},
booktitle = {Proceedings of the 31st ACM International Conference on Information \& Knowledge Management},
pages = {4209–4213},
numpages = {5},
keywords = {friend recommendation, contrastive learning, lbsn, hypergraph},
location = {Atlanta, GA, USA},
series = {CIKM '22}
}