The Fair Graph Anomaly Detection (FairGAD) problem aims to accurately detect anomalous nodes in an input graph while ensuring fairness and avoiding biased predictions against individuals from sensitive subgroups such as gender or political leanings. Fairness in graphs is particularly crucial in anomaly detection areas such as misinformation detection in search/ranking systems, where decision outcomes can significantly affect individuals. However, the current literature does not comprehensively discuss this problem, nor does it provide realistic datasets that encompass actual graph structures, anomaly labels, and sensitive attributes for research in FairGAD. To bridge this gap, we introduce a formal definition of the FairGAD problem and present two novel graph datasets constructed from the globally prominent social media platforms Reddit and Twitter. These datasets comprise 1.2 million and 400,000 edges associated with 9,000 and 47,000 nodes, respectively, and leverage political leanings as sensitive attributes and misinformation spreaders as anomaly labels. We demonstrate that our FairGAD datasets significantly differ from the synthetic datasets used currently by the research community. These new datasets offer significant values for FairGAD by providing realistic data that captures the intricacies of social networks. Using our datasets, we investigate the performance-fairness trade-off in eleven existing GAD and non-graph AD methods on five state-of-the-art fairness methods, which sheds light on their effectiveness and limitations in addressing the FairGAD problem.

公平图异常检测（FairGAD）问题是在确保公平性、避免对敏感子群体（如性别或政治倾向）的个体产生偏见的同时，准确检测输入图中的异常节点。本研究通过引入公平图异常检测问题的正式定义和来自全球知名社交媒体平台Reddit和Twitter的两个新型图数据集，解决了文献中尚未全面讨论该问题且未提供现实数据集的问题。我们的数据集包含了120万条和40万条边，分别与9000个和47000个节点相关联，将政治倾向作为敏感属性，并将谣言传播者定义为异常标签。在这些数据集的基础上，我们研究了11种现有的图异常检测和非图异常检测方法以及五种最先进的公平性方法的性能与公平性权衡问题，揭示了它们在解决公平图异常检测问题中的有效性和局限性。

公平图异常检测：问题、新数据集和评估