Adversarial Attacks on Fairness of Graph Neural Networks
Authors: Binchi Zhang, Yushun Dong, Chen Chen, Yada Zhu, Minnan Luo, Jundong Li
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental study demonstrates that G-Fair Attack successfully corrupts the fairness of different types of GNNs while keeping the attack unnoticeable. Our study on fairness attacks sheds light on potential vulnerabilities in fairness-aware GNNs and guides further research on the robustness of GNNs in terms of fairness. Experimental Evaluation. We conduct extensive experiments on three real-world datasets with four types of victim models and verify that our proposed G-Fair Attack successfully jeopardizes the fairness of various fairness-aware GNNs with an unnoticeable effect on prediction utility. |
| Researcher Affiliation | Collaboration | Binchi Zhang1, Yushun Dong1, Chen Chen1, Yada Zhu2, Minnan Luo3, Jundong Li1 1University of Virginia 2IBM Research 3Xi an Jiaotong University |
| Pseudocode | Yes | With rt(u, v), the pseudocode of our proposed attack algorithm is shown in Appendix C.1. |
| Open Source Code | Yes | The open-source code is available at https://github.com/zhangbinchi/G-Fair Attack. |
| Open Datasets | Yes | We adopt three prevalent real-world datasets, i.e., Facebook (Leskovec & Mcauley, 2012), Credit (Agarwal et al., 2021), and Pokec (Dai & Wang, 2021; Dong et al., 2022a) to test the effectiveness of G-Fair Attack. In our experiment implementation, we adopt the Py GDebias library (Dong et al., 2023a) to load these datasets. |
| Dataset Splits | Yes | Table 3: Dataset statistics. ... #Train/% #Validation/% #Test/% |
| Hardware Specification | Yes | All experiments are implemented on an Nvidia RTX A6000 GPU. |
| Software Dependencies | Yes | PyTorch == 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 |
| Experiment Setup | Yes | We provide the hyperparameter settings of G-Fair Attack in Table 5, and the hyperparameter settings of test GNNs in Table 4. |