726 citations · 1.2k across the 16 of their papers we have counts for
5 papers · 1 filter
Learning on Large-scale Text-attributed Graphs via Variational Inference
Jianan Zhao, Meng Qu, Chaozhuo Li +5
This paper studies learning on text-attributed graphs (TAGs), where each node is associated with a text description. An ideal solution for such a problem would be integrating both…
KGNN: Harnessing Kernel-based Networks for Semi-supervised Graph Classification
Wei Ju, Junwei Yang, Meng Qu +3
This paper studies semi-supervised graph classification, which is an important problem with various applications in social network analysis and bioinformatics. This problem is typi…
Neural Structured Prediction for Inductive Node Classification
Meng Qu, Huiyu Cai, Jian Tang
This paper studies node classification in the inductive setting, i.e., aiming to learn a model on labeled training graphs and generalize it to infer node labels on unlabeled test g…
Structured Multi-task Learning for Molecular Property Prediction
Shengchao Liu, Meng Qu, Zuobai Zhang +2
Multi-task learning for molecular property prediction is becoming increasingly important in drug discovery. However, in contrast to other domains, the performance of multi-task lea…
TorchDrug: A Powerful and Flexible Machine Learning Platform for Drug Discovery
Zhaocheng Zhu, Chence Shi, Zuobai Zhang +12
Machine learning has huge potential to revolutionize the field of drug discovery and is attracting increasing attention in recent years. However, lacking domain knowledge (e.g., wh…