Directed graph convolution network with multi-layer perceptron

Requirements

Our project is developed using Python 3.7 PyTorch 1.5.0 with CUDA10.2. We recommend you to use anaconda for dependency configuration.

First create an anaconda environment called DGMP by

conda create -n DGMP python=3.7
conda activate DGMP

Then, you need to install torch manually to fit in with your server environment (e.g. CUDA version). run

conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/
conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main/
conda config --set show_channel_urls yes
conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/pytorch/
conda install pytorch==1.5.0 torchvision==0.6.0 cudatoolkit=10.2 -c pytorch

Besides, torch-scatter and torch-sparse are required for dealing with sparse graph. For these two packages, please follow their official instruction torch-scatter and torch-sparse.

cd DGMP
pip install torch-geometric
pip install torch-scatter 

Run

cd code
python gcn.py --gpu-no 0 --dataset cancer
python DGMP.py --gpu-no 0 --dataset cancer
python MLP.py --gpu-no 0 --dataset cancer

cpu
python gcn.py --gpu-no -1 --dataset cancer
python DGMP.py --gpu-no -1 --dataset cancer
python MLP.py --gpu-no -1 --dataset cancer

License

DGMP is released under the MIT License. See the LICENSE file for more details.

Acknowledgements

We thank the authors of following works for opening source their excellent codes.DiGCN