Zhao-Yu Liu

Fair Graph Learning Needs Expressiveness: Rethinking Fairness from the Spectral Perspective

It is formally proved that GNN architectures lacking spectral expressiveness impose strict constraints on the representation space, so that harmful linear correlations between sensitive attributes and target prediction logits are preserved whenever a low-expressive backbone is paired with a debiasing operator acting wi...