Signal propagation in complex networks drives epidemics, is responsible for information
going viral, promotes trust and facilitates moral behavior in social groups, enables the …

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

As a core technology of Intelligent Transportation System, traffic flow prediction has a wide
range of applications. The fundamental challenge in traffic flow prediction is to effectively …

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 …

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

Multivariate Time Series (MTS) forecasting plays a vital role in a wide range of applications.
Recently, Spatial-Temporal Graph Neural Networks (STGNNs) have become increasingly …

Spatial-temporal data forecasting of traffic flow is a challenging task because of complicated
spatial dependencies and dynamical trends of temporal pattern between different roads …

Spatial-temporal forecasting has attracted tremendous attention in a wide range of
applications, and traffic flow prediction is a canonical and typical example. The complex and …

Graph representation learning aims to effectively encode high-dimensional sparse graph-
structured data into low-dimensional dense vectors, which is a fundamental task that has …

Traffic forecasting is one of the most popular spatio-temporal tasks in the field of machine
learning. A prevalent approach in the field is to combine graph convolutional networks and …