이연창’s paper has been accepted in

Title: Towards Fair Graph Anomaly Detection: Problem, New Datasets, and Evaluation
Author: Neng Kai Nigel Neo, Yeon-Chang Lee, Yiqiao Jin, Sang-Wook Kim, and Srijan Kumar
Abstract
The Fair Graph Anomaly Detection (FairGAD) problem aims to ac- curately 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, re- spectively, 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. Code

and datasets are available at https://github.com/nigelnnk/FairGAD.