A PyTorch implementation of "Fair Attribute Completion on Graph with Missing Attributes" [Paper]

torch==1.12.0
DGL=0.9.0
scikit-learn==1.1.1

or you can directly create a conda environment by environment.yml

conda env create -f environment.yml

1. We only care about the epochs that the accuracy and roc score 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.

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

bash scripts/pokec_z/train_fairAC.sh

We thank @oxkitsune for the detailed reimplementation.

If you find this repo useful, please consider citing:

@inproceedings{
guo2023fair,
title={Fair Attribute Completion on Graph with Missing Attributes},
author={Dongliang Guo and Zhixuan Chu and Sheng Li},
booktitle={The Eleventh International Conference on Learning Representations },
year={2023},
url={https://openreview.net/forum?id=9vcXCMp9VEp}
}