3 papers
cs.LG2026
SAOT: Self-Supervised Continual Graph Learning with Structure-Aware Optimal Transport
Yuting Zhang, Yanbei Liu, Zhitao Xiao +3
Self-supervised Continual Graph Learning (CGL) aims to successively learn from a graph sequence with different tasks without label supervision - a paradigm that has attracted wides…
cs.LG2025
HGOT: Self-supervised Heterogeneous Graph Neural Network with Optimal Transport
Yanbei Liu, Chongxu Wang, Zhitao Xiao +3
Heterogeneous Graph Neural Networks (HGNNs), have demonstrated excellent capabilities in processing heterogeneous information networks. Self-supervised learning on heterogeneous gr…
cs.AI2024
Multi-Scale Subgraph Contrastive Learning
Yanbei Liu, Yu Zhao, Xiao Wang +2
Graph-level contrastive learning, aiming to learn the representations for each graph by contrasting two augmented graphs, has attracted considerable attention. Previous studies usu…