Xiuxin Hao

From Semantics to Spectrum: A New Lens on Graph Augmentation Strategy

Graph augmentation is a cornerstone of effective graph contrastive learning, yet existing methods often rely on random designed perturbations, which may distort latent semantics and impair representation quality. In this work, we argue that semantic consistency can be effectively approximated by low