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GEAR-WSDM22: Node Representation Learning for Graph Counterfactual Fairness

Code for the WSDM 2022 paper Learning Fair Node Representations with Graph Counterfactual Fairness.

Environment

Python 3.6
Pytorch 1.6.0
Sklearn 0.22
Numpy 1.18.3
Torch-geometric 1.3.0

Dataset

Consider ./ as /src. Datasets can be found in ../dataset/

Run Experiment

Learning node representation

python main.py --experiment_type train

The subgraphs will be generated under ./graphFair_subgraph/ at the first time of running. If the files already exist, the subgraph data will be directly loaded. The true counterfactuals for evaluation are in ./graphFair_subgraph/cf/, and the augmented data is in ./graphFair_subgraph/aug/. The trained GEAR model can be saved in ./models_save/.

Refenrences

The code is the implementation of this paper:

[1] J. Ma, R. Guo, M. Wan, L. Yang, A. Zhang, and J. Li. Learning fair node representations with graph counterfactual fairness. In Proceedings of the 15th WSDM, 2022

Acknowledgement: The code in this work is developed based on part of the code in the following papers:

[2] Chirag Agarwal, Himabindu Lakkaraju, and Marinka Zitnik. Towards a unified framework for fair and stable graph representation learning. arXiv preprint arXiv:2102.13186, 2021.
[3] Jiao Y, Xiong Y, Zhang J, et al. Sub-graph contrast for scalable self-supervised graph representation learning[C]//2020 IEEE International Conference on Data Mining (ICDM). IEEE, 2020: 222-231.
[4] Kipf T N, Welling M. Variational graph auto-encoders[J]. arXiv preprint arXiv:1611.07308, 2016.

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