FairGNN-Bagging: Balancing model performance and algorithmic fairness in graph neural networks using ensemble learning - Valdivia, Juan Carlos Elias Obando; Barros, Marcel Rodrigues de; Correia, Artur Jordão Lima; Costa, Anna Helena Reali (Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC), 2025)
Graph Neural Networks (GNNs) are powerful tools for learning from graph-structured data, with applications in social networks, bioinformatics, and fraud detection. However, GNNs can inherit and amplify biases present in data, leading to unfair predictions regarding sensitive attributes such as gender or race. To mitigate these issues, fairness-aware frameworks like NIFTY and its variant with Biased Edge Dropout have been proposed. In this work, we introduce FairGNN-Bagging, an ensemble framework that combines NIFTY with Bagging to improve fairness in node classification tasks. Our method uses graph perturbations to generate an augmented graph dataset to train an ensemble of GNNs in parallel. These models are then aggregated via majority voting or fairness-aware selective voting. We evaluate our approach on three real-world datasets, showing that it achieves better fairness metrics while preserving or improving predictive performance.