Graph representation learning on vast datasets, like web data, has made significant strides.
However, the associated computational and storage overheads raise concerns. In sight of …

This paper proposes a two-stage framework named ST-PAD for spatio-temporal fluid
dynamics modeling in the field of earth sciences, aiming to achieve high-precision …

Building natural language interfaces typically uses a semantic parser to parse the user's
natural language and convert it into structured\textbf {S} emantic\textbf {L} ogic\textbf {F} …

Spatio-temporal (ST) prediction plays a pivotal role in earth sciences, such as
meteorological prediction, urban computing. Adequate high-quality data, coupled with deep …

Credit card fraud poses a significant threat to the economy. While Graph Neural Network
(GNN)-based fraud detection methods perform well, they often overlook the causal effect of a …

The ever-increasing sensor service, though opening a precious path and providing a deluge
of earth system data for deep-learning-oriented earth science, sadly introduce a daunting …

Generalizable and transferrable graph representation learning endows graph neural
networks (GNN) with the ability to extrapolate potential test distributions. Nonetheless …

In this paper, we present a novel method to significantly enhance the computational
efficiency of Adaptive Spatial-Temporal Graph Neural Networks (ASTGNNs) by introducing …

Modeling the evolution of physical dynamics is a foundational problem in science and
engineering, and it is regarded as the modeling of an operator mapping between infinite …

Satellite cloud image sequences contain rich spatial and temporal information, and
forecasting future cloud image sequences is of great significance for meteorological …