Fair GNN

Fair Graph Neural Networks (GNNs) research focuses on mitigating biases amplified by GNNs during graph data processing, aiming to ensure equitable predictions across different demographic groups. Current efforts concentrate on addressing biases stemming from imbalanced data distributions, local homophily variations, and structural features within the graph, employing techniques like re-balancing, counterfactual learning, and sensitive attribute disentanglement within various GNN architectures. This field is crucial for responsible deployment of GNNs in high-stakes applications like loan applications or criminal justice, where biased predictions can have significant societal consequences.