Code for "Heterogeneous Graph Neural Network for Privacy-Preserving Recommendation"
Our experimental environments are listed in environments.yaml, you can create a virtual environment with conda and run the following order.
conda env create -f environments.yaml
Enter the virtual environment and run the requirements.txt.
pip install -r requirements.txt
Run the following order to train our model with setting custom parameters.
python3 main.py -e=50 -ef=0.5 -et=0.5 --task='lp' -d='acm' -g=0
Some of the code was forked from the following repositories: