Fairness-aware Graph Neural Networks (GNNs) often face a challenging
trade-off, where prioritizing fairness may require compromising utility. In
this work, we re-examine fairness through the lens of spectral graph theory,
aiming to reconcile fairness and utility within the framework of spectral graph
learning. We explore the correlation between sensitive features and spectrum in
GNNs, using theoretical analysis to delineate the similarity between original
sensitive features and those after convolution under different spectrum. Our
analysis reveals a reduction in the impact of similarity when the eigenvectors
associated with the largest magnitude eigenvalue exhibit directional
similarity. Based on these theoretical insights, we propose FUGNN, a novel
spectral graph learning approach that harmonizes the conflict between fairness
and utility. FUGNN ensures algorithmic fairness and utility by truncating the
spectrum and optimizing eigenvector distribution during the encoding process.
The fairness-aware eigenvector selection reduces the impact of convolution on
sensitive features while concurrently minimizing the sacrifice of utility.
FUGNN further optimizes the distribution of eigenvectors through a transformer
architecture. By incorporating the optimized spectrum into the graph
convolution network, FUGNN effectively learns node representations. Experiments
on six real-world datasets demonstrate the superiority of FUGNN over baseline
methods. The codes are available at this https URL

通过谱图理论重新审视公平性，并在谱图学习框架中协调公平性和实用性的权衡，提出了一种新颖的谱图学习方法 FUGNN，通过截断谱并在编码过程中优化特征向量分布，建立起算法公平性和实用性之间的平衡。

FUGNN: 图形神经网络中的公平性和效用的协调

FUGNN: Harmonizing Fairness and Utility in Graph Neural Networks

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。