Dynamic Graph Neural Networks (Dynamic GNNs) have emerged as powerful tools for modeling real-world networks with evolving topologies and node attributes over time. A survey by Professors Zhewei Wei, ...

With the rapid advancement of socioeconomic development and urbanization, urban vehicle populations have witnessed an exponential growth trend, accompanied by continuously increasing traffic volume.

Accurate spatiotemporal forecasting is of great significance in fields such as public health, environmental monitoring, and smart cities. In recent years, researchers have widely adopted ...