Towards a Unified Framework for Fair and Stable Graph Representation Learning

This repository contains source code necessary to reproduce some of the main results in the paper:

If you use this software, please consider citing:

@article{agarwal2021unified,
  title={Towards a Unified Framework for Fair and Stable Graph Representation Learning},
  author={Chirag Agarwal and Himabindu Lakkaraju* and Marinka Zitnik*},
  year={2021},
  booktitle={arXiv},
}

Our framework NIFTY can learn node representations that are both fair and stable (i.e., invariant to the sensitive attribute value and perturbations to the graph structure and non-sensitive attributes) by maximizing the similarity between representations from diverse augmented graphs.

1. Setup

Installing software

This repository is built using PyTorch. You can install the necessary libraries by pip installing the requirements text file pip install -r ./requirements.txt After installing the packages from the requirements.txt, install the PyTorch Geometric packages following the instructions from here.

Note: We ran our codes using python=3.7.9

2. Datasets

We ran our experiments on three high-stake read-world datasets. All the data are present in the './datasets' folder. Due to space constraints the edge file of the credit dataset is zipped.

3. Usage

The main scripts running the experiments on the state-of-the-art GNNs and their NIFTY-augmented counterparts is in nifty_sota_gnn.py

Examples

Script 1: Evaluate fairness and stability performance of GCN (for German Graph dataset) python nifty_sota_gnn.py --dropout 0.5 --hidden 16 --lr 1e-3 --epochs 1000 --model gcn --dataset german --seed 1

The AUCROC of estimator: 0.7605
Parity: 0.3952 | Equality: 0.2731
F1-score: 0.8078
CounterFactual Fairness: 0.2960
Robustness Score: 0.1160

Script 2: Evaluate fairness and stability performance of NIFTY-GCN (for German Graph dataset) python nifty_sota_gnn.py --drop_edge_rate_1 0.001 --drop_edge_rate_2 0.001 --drop_feature_rate_1 0.1 --drop_feature_rate_2 0.1 --dropout 0.5 --hidden 16 --lr 1e-3 --epochs 1000 --model ssf --encoder gcn --dataset german --sim_coeff 0.6 --seed 1

The AUCROC of estimator: 0.7205
Parity: 0.0104 | Equality: 0.0199
F1-score: 0.8235
CounterFactual Fairness: 0.0
Robustness Score: 0.0

Script 3: Evaluate fairness and stability performance of FairGCN baseline (for German Graph dataset) python baseline_fairGNN.py --dropout 0.5 --hidden 16 --lr 1e-3 --epochs 1000 --dataset german --seed 1 --model gcn

The AUCROC of estimator: 0.7549
Parity: 0.2763 | Equality: 0.1723
F1-score: 0.8251
CounterFactual Fairness: N/A
Robustness Score: 0.0440

Script 4: Evaluate fairness and stability performance of RobustGCN (for German Graph dataset) python nifty_sota_gnn.py --dropout 0.5 --hidden 16 --lr 1e-3 --epochs 1000 --model rogcn --dataset german --seed 5

The AUCROC of estimator: 0.6230
Parity: 0.2449 | Equality: 0.2048
F1-score: 0.6143
CounterFactual Fairness: 0.0880
Robustness Score: 0.1320

4. Licenses

Note that the code in this repository is licensed under MIT License. Please carefully check them before use.

5. Questions?

If you have questions/suggestions, please feel free to email or create github issues.