zhangbinchi / G-FairAttack

Open-source code for "Adversarial Attacks on Fairness of Graph Neural Networks" (ICLR 2024).

G-FairAttack

The code is associated with Adversarial Attacks on Fairness of Graph Neural Networks (ICLR 2024).

Requirements

• python == 3.9.13
• torch == 1.11.0
• torch-geometric == 2.0.4
• numpy == 1.21.5
• numba == 0.56.3
• networkx == 2.8.4
• scikit-learn == 1.1.1
• scipy == 1.9.1
• dgl == 0.9.1
• deeprobust == 0.2.5

Attack

Conduct fairness evasion attack on the structure of Facebook dataset.

python attack.py --dataset facebook

Conduct fairness poisoning attack on the structure of Facebook dataset.

python attack.py --dataset facebook --poisoning

Please refer to Appendix E for the hyperparameter settings.

Evaluation

After running the attack program, replace the 'Path' string in each test program with the real path of the poisoned adjacency matrix.

Evaluate the attack on a victim model (e.g., GCN)

1. Replace the 'Path' at line 53 in test_gcn_evasion.py with the path of the perturbed adjacency matrix.
2. Run

python test_gcn_evasion.py

Please refer to Appendix E for the hyperparameter settings.