A comprehensive survey on deep graph representation learning
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 …
structured data into low-dimensional dense vectors, which is a fundamental task that has …
A survey on graph neural networks for time series: Forecasting, classification, imputation, and anomaly detection
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) …
generated in great volume by both physical sensors and online processes (virtual sensors) …
Pdformer: Propagation delay-aware dynamic long-range transformer for traffic flow prediction
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 …
range of applications. The fundamental challenge in traffic flow prediction is to effectively …
Spatio-temporal graph neural networks for predictive learning in urban computing: A survey
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 …
generated and recorded in smart cities. Forecasting the evolution patterns of spatio-temporal …
Graph neural controlled differential equations for traffic forecasting
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 …
learning. A prevalent approach in the field is to combine graph convolutional networks and …
Deciphering spatio-temporal graph forecasting: A causal lens and treatment
Abstract Spatio-Temporal Graph (STG) forecasting is a fundamental task in many real-world
applications. Spatio-Temporal Graph Neural Networks have emerged as the most popular …
applications. Spatio-Temporal Graph Neural Networks have emerged as the most popular …
Spatio-temporal adaptive embedding makes vanilla transformer sota for traffic forecasting
With the rapid development of the Intelligent Transportation System (ITS), accurate traffic
forecasting has emerged as a critical challenge. The key bottleneck lies in capturing the …
forecasting has emerged as a critical challenge. The key bottleneck lies in capturing the …
Largest: A benchmark dataset for large-scale traffic forecasting
Road traffic forecasting plays a critical role in smart city initiatives and has experienced
significant advancements thanks to the power of deep learning in capturing non-linear …
significant advancements thanks to the power of deep learning in capturing non-linear …
Msdr: Multi-step dependency relation networks for spatial temporal forecasting
Spatial temporal forecasting plays an important role in improving the quality and
performance of Intelligent Transportation Systems. This task is rather challenging due to the …
performance of Intelligent Transportation Systems. This task is rather challenging due to the …
A decomposition dynamic graph convolutional recurrent network for traffic forecasting
Our daily lives are greatly impacted by traffic conditions, making it essential to have accurate
predictions of traffic flow within a road network. Traffic signals used for forecasting are …
predictions of traffic flow within a road network. Traffic signals used for forecasting are …