EqGNN: Equalized Node Opportunity in Graphs

This repository provides a reference implementation of EqGNN as described in the paper:

EqGNN: Equalized Node Opportunity in Graphs

The EqGNN algorithm is a fair graph neural network for equalized opportunity predictions.

Requirements

• torch==1.7.1
• pytorch_lightning==1.0.7
• dgl==0.5.3
• networkx==2.5
• sklearn==0.23.2
• pandas==1.1.5
• numpy==1.19.2
• scipy==1.5.2
• wandb==0.10.12

Usage

python run.py \
    --dataset=${dataset} \
    --sensitive=${sensitive_attribute} \
    --log_path=${log_path} \
    --gpus=${gpus} \
    --lr=${learning_rate} \
    --wd=${weight_decay} \
    --dropout=${dropout} \
    --dim=${embedding_size} \
    --epochs=${epochs} \
    --lmb=${lambda} \
    --gamma=${gamma} \
    --loss=${discriminator_loss} \
    --use_hidden

Cite

Please cite our paper if you use this code in your own work:

@inproceedings{aaai2022-eqgnn,
  title     = {EqGNN: Equalized Node Opportunity in Graphs},
  author    = {Singer, Uriel and Radinsky Kira},
  booktitle = {Proceedings of the AAAI conference on artificial intelligence},
  year      = {2022},
  month     = {2},
}