33 citations · 57 across the 3 of their papers we have counts for
3 papers
cs.LG2022
HCL: Improving Graph Representation with Hierarchical Contrastive Learning
Jun Wang, Weixun Li, Changyu Hou +6
Contrastive learning has emerged as a powerful tool for graph representation learning. However, most contrastive learning methods learn features of graphs with fixed coarse-grained…
cs.LG2021★ 33 cited
Simulated annealing for optimization of graphs and sequences
Xianggen Liu, Pengyong Li, Fandong Meng +5
Optimization of discrete structures aims at generating a new structure with the better property given an existing one, which is a fundamental problem in machine learning. Different…
cs.LG2020★ 24 cited
Learn molecular representations from large-scale unlabeled molecules for drug discovery
Pengyong Li, Jun Wang, Yixuan Qiao +6
How to produce expressive molecular representations is a fundamental challenge in AI-driven drug discovery. Graph neural network (GNN) has emerged as a powerful technique for model…