Awesome

FairGNN

A PyTorch implementation of "Say No to the Discrimination: Learning Fair Graph Neural Networks with Limited Sensitive Attribute Information" (WSDM 2021). [paper]

Requirements

torch==1.2.0
DGL=0.4.3

Run the code

After installation, you can clone this repository

git clone https://github.com/EnyanDai/FariGNN.git
cd FairGNN/src
python train_fairGNN.py \
        --seed=42 \
        --epochs=2000 \
        --model=GCN \
        --sens_number=200 \
        --dataset=pokec_z \
        --num-hidden=128 \
        --acc=0.69 \
        --roc=0.76 \
        --alpha=100 \
        --beta=1

Model Selection

During the training phase, we will select the best epoch based on the performance on the validation set. More speciafically, the selection rules are:

1. We only care about the epochs that the accuracy and roc socre of the FairGNN on the validation set are higher than the thresholds (defined by --acc and --roc).
2. We will select the epoch whose summation of parity and equal opportunity is the smallest.

Data Set

1. Pokec_z and Pokec_n are stored in dataset\pokec as region_job.xxx and region_job_2.xxx, respectively. They are sampled from soc_Pokec.

@inproceedings{takac2012data,
  title={Data analysis in public social networks},
  author={Takac, Lubos and Zabovsky, Michal},
  booktitle={International scientific conference and international workshop present day trends of innovations},
  volume={1},
  number={6},
  year={2012}

2. NBA is stored in dataset\NBA as nba.xxx It is collected with through the Twitter social network and the players' information on Kaggle

Reproduce the results

Please use DGL 0.4.3, the version of the DGL can affect the results a lot. All the hyper-parameters settings are included in src\scripts folder.

To reproduce the performance reported in the paper, you can run the bash files in folder src\scripts.

bash scripts/pokec_z/train_fairGCN.sh

Here are some example results:

PyG verision of FairGNN

Thanks to the great work of the PyG-Debias contributors, the PyG verision of FairGNN is available now. Please check the PyG-Debias package.

Cite

If you find this repo to be useful, please cite our paper. Thank you.

@inproceedings{dai2021say,
  title={Say No to the Discrimination: Learning Fair Graph Neural Networks with Limited Sensitive Attribute Information},
  author={Dai, Enyan and Wang, Suhang},
  booktitle={Proceedings of the 14th ACM International Conference on Web Search and Data Mining},
  pages={680--688},
  year={2021}
}