Graph Neural Networks (GNNs) have shown remarkable success in various graph-based learning tasks. However, recent studies have raised concerns about fairness and privacy issues in GNNs, highlighting the potential for biased or discriminatory outcomes and the vulnerability of sensitive information. This paper presents a comprehensive investigation of fairness and privacy in GNNs, exploring the impact of various fairness-preserving measures on model performance. We conduct experiments across diverse datasets and evaluate the effectiveness of different fairness interventions. Our analysis considers the trade-offs between fairness, privacy, and accuracy, providing insights into the challenges and opportunities in achieving both fair and private graph learning. The results highlight the importance of carefully selecting and combining fairness-preserving measures based on the specific characteristics of the data and the desired fairness objectives. This study contributes to a deeper understanding of the complex interplay between fairness, privacy, and accuracy in GNNs, paving the way for the development of more robust and ethical graph learning models.

翻译：图神经网络（GNNs）在各种基于图的学习任务中取得了显著成功。然而，近期研究对GNN中的公平性与隐私问题提出了担忧，指出其可能产生偏见或歧视性结果，以及敏感信息易受攻击的脆弱性。本文对GNN中的公平性与隐私进行了全面研究，探讨了多种公平性保护措施对模型性能的影响。我们在多个数据集上进行了实验，评估了不同公平性干预措施的有效性。我们的分析考虑了公平性、隐私性与准确性之间的权衡，为实现公平且隐私保护的图学习提供了挑战与机遇的见解。研究结果强调，需根据数据的具体特征和期望的公平性目标，谨慎选择和组合公平性保护措施。本研究有助于更深入地理解GNN中公平性、隐私性与准确性之间复杂的相互作用，为开发更稳健且符合伦理的图学习模型铺平道路。