Secure Deep Graph Generation with Link Differential Privacy

This repository is the PyTorch implementation of DPGGan (IJCAI 2021).

arXiv

If you make use of the code/experiment, please cite our paper (Bibtex below).

@inproceedings{yang2020secure,
    title={Secure Deep Graph Generation with Link Differential Privacy},
    author={Carl Yang and Haonan Wang and Ke Zhang and Liang Chen and Lichao Sun},
    year={2021},
    booktitle={The International Joint Conference on Artificial Intelligence (IJCAI)},
}

Contact: Haonan Wang (haonan3@illinois.edu), Carl Yang (yangji9181@gmail.com)

Installation

Install PyTorch following the instuctions on the [official website] (https://pytorch.org/). The code has been tested over PyTorch 1.1.0 versions.

Then install the other dependencies.

conda env create -f environment.yml

conda activate dpggan

pip install -r requirements.txt

Test run

Unzip the dataset file

unzip data.zip

and run

sh run.sh

Default parameters are not the best performing-hyper-parameters. Hyper-parameters need to be specified through the commandline arguments.

For graph classification experiment and link prediction experiment, please refer run_graph_classification_exp.sh and run_link_classification_exp.sh.