Fair Graph

Fair graph learning aims to develop graph-based machine learning models that avoid discriminatory outcomes against specific subgroups defined by sensitive attributes (e.g., race, gender). Current research focuses on mitigating bias through techniques like data augmentation, sensitive attribute disentanglement, and localized neighborhood fairness, often employing graph neural networks (GNNs) as the underlying model architecture. This field is crucial for ensuring equitable outcomes in applications such as recommendation systems, anomaly detection, and social network analysis, where biased algorithms can have significant real-world consequences. The development of standardized benchmarks and datasets is also a key area of ongoing work to facilitate robust and comparable evaluations of fairness-aware models.