Time series are the primary data type used to record dynamic system measurements and
generated in great volume by both physical sensors and online processes (virtual sensors) …

Since the onset of the COVID-19 pandemic, there has been a growing interest in studying
epidemiological models. Traditional mechanistic models mathematically describe the …

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 …

Graph representation learning has been a very active research area in recent years. The
goal of graph representation learning is to generate graph representation vectors that …

Graph Neural Networks have gained huge interest in the past few years. These powerful
algorithms expanded deep learning models to non-Euclidean space and were able to …

Modeling complex spatiotemporal dependencies in correlated traffic series is essential for
traffic prediction. While recent works have shown improved prediction performance by using …

Complex networks pervade various real-world systems, from the natural environment to
human societies. The essence of these networks is in their ability to transition and evolve …

In recent years, Dynamic Graph (DG) representations have been increasingly used for
modeling dynamic systems due to their ability to integrate both topological and temporal …

As a decisive part in the success of Mobility-as-a-Service (MaaS), spatio-temporal dynamics
modeling on mobility networks is a challenging task particularly considering scenarios …

With the rapid development of data acquisition and storage technology, spatio-temporal (ST)
data in various fields are growing explosively, so many ST prediction methods have …