Fairness-aware graph neural networks (GNNs) have gained a surge of attention
as they can reduce the bias of predictions on any demographic group (e.g.,
female) in graph-based applications. Although these methods greatly improve the
algorithmic fairness of GNNs, the fairness can be easily corrupted by carefully
designed adversarial attacks. In this paper, we investigate the problem of
adversarial attacks on fairness of GNNs and propose G-FairAttack, a general
framework for attacking various types of fairness-aware GNNs in terms of
fairness with an unnoticeable effect on prediction utility. In addition, we
propose a fast computation technique to reduce the time complexity of
G-FairAttack. The experimental study demonstrates that G-FairAttack
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. The open-source code is available at
this https URL

对公平性感知的图神经网络进行了对抗攻击研究，提出了 G-FairAttack 攻击框架，可有效地破坏不同类型的图神经网络的公正性而保持攻击不被察觉，以此揭示了公平性感知的图神经网络潜在的漏洞，并引导进一步研究图神经网络在公平性方面的鲁棒性。