1 citations · 1 across the 2 of their papers we have counts for
3 papers
cs.LG2022★ 1 cited
Informative Pseudo-Labeling for Graph Neural Networks with Few Labels
Yayong Li, Jie Yin, Ling Chen
Graph Neural Networks (GNNs) have achieved state-of-the-art results for semi-supervised node classification on graphs. Nevertheless, the challenge of how to effectively learn GNNs…
cs.LG2021
RMNA: A Neighbor Aggregation-Based Knowledge Graph Representation Learning Model Using Rule Mining
Ling Chen, Jun Cui, Xing Tang +4
Although the state-of-the-art traditional representation learning (TRL) models show competitive performance on knowledge graph completion, there is no parameter sharing between the…
cs.LG2021
Group-Aware Graph Neural Network for Nationwide City Air Quality Forecasting
Ling Chen, Jiahui Xu, Binqing Wu +4
The problem of air pollution threatens public health. Air quality forecasting can provide the air quality index hours or even days later, which can help the public to prevent air p…