Traffic forecasting is important for the success of intelligent transportation systems. Deep
learning models, including convolution neural networks and recurrent neural networks, have …

Graph convolutional networks (GCNs) have a strong ability to learn graph representation
and have achieved good performance in a range of applications, including social …

With recent advances in sensing technologies, a myriad of spatio-temporal data has been
generated and recorded in smart cities. Forecasting the evolution patterns of spatio-temporal …

In recent years, various deep learning architectures have been proposed to solve complex
challenges (eg spatial dependency, temporal dependency) in traffic domain, which have …

Motivation Thanks to the increasing availability of drug–drug interactions (DDI) datasets and
large biomedical knowledge graphs (KGs), accurate detection of adverse DDI using …

Every day, our living city produces a tremendous amount of spatial-temporal data, involved
with multiple sources from the individual scale to the city scale. Undoubtedly, such massive …

The prediction of traffic flow is a challenging yet crucial problem in spatial-temporal analysis,
which has recently gained increasing interest. In addition to spatial-temporal correlations …

Due to the widespread applications in real-world scenarios, metro ridership prediction is a
crucial but challenging task in intelligent transportation systems. However, conventional …

Deep learning models are modern tools for spatio-temporal graph (STG) forecasting.
Though successful, we argue that data scarcity is a key factor limiting their recent …

Recent studies have significantly improved the prediction accuracy of travel demand using
graph neural networks. However, these studies largely ignored uncertainty that inevitably …