Towards Fair Graph Anomaly Detection: Problem, New Datasets, and
  Evaluation
Neng Kai Nigel Neo Yeon-Chang Lee Yiqiao Jin Sang-Wook Kim Srijan Kumar
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.