Researchers Develop Fairer Graph Neural Networks by Reducing Structural Bias

Researchers propose a new method to reduce bias in Graph Neural Networks (GNNs) by editing graph structures to minimize discrimination based on sensitive attributes while maintaining predictive accuracy. The two-phase approach uses contrastive learning to improve both fairness and classification performance across real-world datasets.