  The Fair Graph Anomaly Detection (FairGAD) problem aims to accurately detect
anomalous nodes in an input graph while avoiding biased predictions against
individuals from sensitive subgroups. 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. To bridge this gap, we introduce a formal definition of the FairGAD
problem and present two novel datasets constructed from the 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 by the research community. Using our datasets,
we investigate the performance-fairness trade-off in nine existing GAD and
non-graph AD methods on five state-of-the-art fairness methods. Our code and
datasets are available at https://github.com/nigelnnk/FairGAD
