Year: 2024
Type: conference-paper
Abstract: 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. Code and datasets are available at https://github.com/nigelnnk/FairGAD.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Reduced inequalities
Open Access status: bronze
Awards: NSF Convergence Accelerator Track F: How Large-Scale Identification and Intervention Can Empower Professional Fact-Checkers to Improve Democracy and Public Health, G2246695849, G36973146, G3904058931, G4700831490, G4848589832, G5377595892, RAPID: Tackling the Psychological Impact of the COVID-19 Crisis, Collaborative Research: SaTC: CORE: Medium: An Incident-Response Approach for Empowering Fact-Checkers, CAREER: Social Response-Powered Misinformation Detection, Robustness, and Correction, NSF Convergence Accelerator Track F: Course Correct: Precision Guidance Against Misinformation, G7889947447, G8525406066
OpenAlex ID: W4403577800