CommGNAS

• CommGNAS is an unsupervised automatic graph representation learning framework based on self-supervised and self-representation learning.

• The framework of CommGNAS is as follows:

News

• 2023.04.28 Our work CommGNAS: Unsupervised Graph Neural Architecture Search for Community Detection. is accepted by TETC 2023.

Installing For Ubuntu 16.04

• Ensure you have installed CUDA 10.2 before installing other packages

1. Nvidia and CUDA 10.2:

[Nvidia Driver] 
https://www.nvidia.cn/Download/index.aspx?lang=cn

[CUDA 10.2 Download and install command] 
#download:
wget https://developer.download.nvidia.com/compute/cuda/10.2/Prod/local_installers/cuda_10.2.89_440.33.01_linux.run
#install:
sudo sh cuda_10.2.89_440.33.01_linux.run

2. Python environment: recommending using Conda package manager to install

conda create -n commgnas python=3.7
source activate commgnas

3. Pytorch 1.8.1: execute the following command in your conda env automsr

pip install torch==1.8.1+cu102 torchvision==0.9.1+cu102 torchaudio==0.8.1 -f https://download.pytorch.org/whl/torch_stable.html

4. Pytorch Geometric 2.0.2: execute the following command in your conda env automsr

pip install torch-scatter==2.0.9 torch-sparse==0.6.12 torch-cluster==1.5.9 torch-spline-conv==1.2.1 torch-geometric==2.0.2 -f https://data.pyg.org/whl/torch-1.8.0+cu102.html

5. Ray 1.7.0: execute the following command in your conda env automsr

pip install ray==1.7.0

Running the Experiment

1.Searching the GNN Architecture for Community Detection

python search_main.py

2.Testing the Optimal Model Designed by CommGNAS for Community Detection

python test_main.py

Citing

If you think AutoMSR is useful tool for you, please cite our paper, thank you for your support:

@ARTICLE{10112632,
  author={Gao, Jianliang and Chen, Jiamin and Oloulade, Babatounde Moctard and Al-Sabri, Raeed and Lyu, Tengfei and Zhang, Ji and Li, Zhao},
  journal={IEEE Transactions on Emerging Topics in Computing}, 
  title={CommGNAS: Unsupervised Graph Neural Architecture Search for Community Detection}, 
  year={2023},
  volume={},
  number={},
  pages={1-12},
  doi={10.1109/TETC.2023.3270181}}