Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Adversarial Attacks on Fairness of Graph Neural Networks
Authors: Binchi Zhang, Yushun Dong, Chen Chen, Yada Zhu, Minnan Luo, Jundong Li
ICLR 2024 | Venue PDF | 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. |