154 citations · 214 across the 11 of their papers we have counts for
6 papers · 1 filter
Few-Shot Graph Learning for Molecular Property Prediction
Zhichun Guo, Chuxu Zhang, Wenhao Yu +4
The recent success of graph neural networks has significantly boosted molecular property prediction, advancing activities such as drug discovery. The existing deep neural network m…
Learning Attribute-Structure Co-Evolutions in Dynamic Graphs
Daheng Wang, Zhihan Zhang, Yihong Ma +4
Most graph neural network models learn embeddings of nodes in static attributed graphs for predictive analysis. Recent attempts have been made to learn temporal proximity of the no…
Calendar Graph Neural Networks for Modeling Time Structures in Spatiotemporal User Behaviors
Daheng Wang, Meng Jiang, Munira Syed +4
User behavior modeling is important for industrial applications such as demographic attribute prediction, content recommendation, and target advertising. Existing methods represent…
Data Augmentation for Graph Neural Networks
Tong Zhao, Yozen Liu, Leonardo Neves +3
Data augmentation has been widely used to improve generalizability of machine learning models. However, comparatively little work studies data augmentation for graphs. This is larg…
Heterogeneous Relational Reasoning in Knowledge Graphs with Reinforcement Learning
Mandana Saebi, Steven Krieg, Chuxu Zhang +2
Path-based relational reasoning over knowledge graphs has become increasingly popular due to a variety of downstream applications such as question answering in dialogue systems, fa…
Graph Few-shot Learning via Knowledge Transfer
Huaxiu Yao, Chuxu Zhang, Ying Wei +5
Towards the challenging problem of semi-supervised node classification, there have been extensive studies. As a frontier, Graph Neural Networks (GNNs) have aroused great interest r…