This paper provides a comprehensive survey of the integration of graph neural networks
(GNN) and deep reinforcement learning (DRL) in end-to-end (E2E) networking solutions …

Precise traffic prediction is crucial in the domain of intelligent transportation. However, the
task of accurately predicting traffic has struggled to keep pace with escalating application …

Landslide susceptibility assessment (LSA) is vital for landslide mitigation and management.
Existing LSA methods only consider local environmental characteristics associated with …

Precise and timely traffic flow prediction holds significant importance in alleviating traffic
congestion. Despite the success of graph convolution traffic flow prediction methods, there is …

In multichannel electroencephalograph (EEG) emotion recognition, most graph-based
studies employ shallow graph model for spatial characteristics learning due to node over …

Understanding and managing public transportation systems require capturing complex
spatio-temporal correlations within datasets. Existing studies often use predefined graphs in …

The exponential increase in Internet of Things (IoT) devices coupled with 6G pushing
towards higher data rates and connected devices has sparked a surge in data …

Accurate traffic prediction contributes significantly to the success of intelligent transportation
systems (ITS), which enables ITS to rationally deploy road resources and enhance the …

Traffic flow prediction is crucial in intelligent transportation systems. Considering the severe
disruptions caused by traffic accidents or congestion, a time series model is developed for …

Predicting traffic flow is central to alleviating congestion, optimizing routes, and supporting
traffic planning and urban management. However, this task remains challenging, particularly …