Unbiased and augmentation-free self-supervised graph representation learning

Ruyue Liu, Rong Yin, Yong Liu, Weiping Wang

Abstract: Highlights•USAF-GCL introduces pseudo-homology as a supervisory signal.•We demonstrate that maximizing pseudo-homology improves the mutual information.•The proposed method outperforms the SOTA methods and requires less time.