21 citations · 27 across the 5 of their papers we have counts for
4 papers · 1 filter
MolKD: Distilling Cross-Modal Knowledge in Chemical Reactions for Molecular Property Prediction
Liang Zeng, Lanqing Li, Jian Li
How to effectively represent molecules is a long-standing challenge for molecular property prediction and drug discovery. This paper studies this problem and proposes to incorporat…
ImGCL: Revisiting Graph Contrastive Learning on Imbalanced Node Classification
Liang Zeng, Lanqing Li, Ziqi Gao +2
Graph contrastive learning (GCL) has attracted a surge of attention due to its superior performance for learning node/graph representations without labels. However, in practice, th…
AutoHEnsGNN: Winning Solution to AutoGraph Challenge for KDD Cup 2020
Jin Xu, Mingjian Chen, Jianqiang Huang +5
Graph Neural Networks (GNNs) have become increasingly popular and achieved impressive results in many graph-based applications. However, extensive manual work and domain knowledge…
AKE-GNN: Effective Graph Learning with Adaptive Knowledge Exchange
Liang Zeng, Jin Xu, Zijun Yao +2
Graph Neural Networks (GNNs) have already been widely used in various graph mining tasks. However, recent works reveal that the learned weights (channels) in well-trained GNNs are…