Biased Edge Dropout in NIFTY for Fair Graph Representation Learning
Federico Caldart, Luca Pasa, Luca Oneto, Alessandro Sperduti, Nicolò Navarin · 2022
Graph Neural Networks (GNNs) are nowadays widely used in many real-world applications.Nonetheless, the data relationships can be a source of biases based on sensitive attributes (e.g., gender or ethnicity).Several methods have been proposed to learn fair graph node representations.In this work we extend NIFTY, an approach that exploits additional terms in the loss function based on perturbing the input data to enforce the fairness of the GNNs.In particular, we exploit a biased perturbation of the adjacency matrix of the graph able to reduce the edge homophily.We show the effectiveness of our approach in four real-world graph datasets.