This paper introduces BPR+, a novel loss function for graph self-supervised learning that extends Bayesian Personalized Ranking (BPR) by incorporating even-hop paths to better capture global connectivity and topological structure.

We propose a self-supervised coloring learning framework for heterophilic graph representation, which effectively captures both local and global structures without relying on delicate augmentation strategies.