Exgc: Bridging efficiency and explainability in graph condensation
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 …
However, the associated computational and storage overheads raise concerns. In sight of …
Spatio-temporal fluid dynamics modeling via physical-awareness and parameter diffusion guidance
H Wu, F Xu, Y Duan, Z Niu, W Wang, G Lu… - arXiv preprint arXiv …, 2024 - arxiv.org
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 …
dynamics modeling in the field of earth sciences, aiming to achieve high-precision …
Slfnet: Generating semantic logic forms from natural language using semantic probability graphs
H Wu, F Xu - arXiv preprint arXiv:2403.19936, 2024 - arxiv.org
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} …
natural language and convert it into structured\textbf {S} emantic\textbf {L} ogic\textbf {F} …
NuwaDynamics: Discovering and Updating in Causal Spatio-Temporal Modeling
Spatio-temporal (ST) prediction plays a pivotal role in earth sciences, such as
meteorological prediction, urban computing. Adequate high-quality data, coupled with deep …
meteorological prediction, urban computing. Adequate high-quality data, coupled with deep …
CaT-GNN: Enhancing Credit Card Fraud Detection via Causal Temporal Graph Neural Networks
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 …
(GNN)-based fraud detection methods perform well, they often overlook the causal effect of a …
DynST: Dynamic Sparse Training for Resource-Constrained Spatio-Temporal Forecasting
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 …
of earth system data for deep-learning-oriented earth science, sadly introduce a daunting …
Fortune favors the invariant: Enhancing GNNs' generalizability with Invariant Graph Learning
Generalizable and transferrable graph representation learning endows graph neural
networks (GNN) with the ability to extrapolate potential test distributions. Nonetheless …
networks (GNN) with the ability to extrapolate potential test distributions. Nonetheless …
Pre-Training Identification of Graph Winning Tickets in Adaptive Spatial-Temporal Graph Neural Networks
In this paper, we present a novel method to significantly enhance the computational
efficiency of Adaptive Spatial-Temporal Graph Neural Networks (ASTGNNs) by introducing …
efficiency of Adaptive Spatial-Temporal Graph Neural Networks (ASTGNNs) by introducing …
Neural Manifold Operators for Learning the Evolution of Physical Dynamics
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 …
engineering, and it is regarded as the modeling of an operator mapping between infinite …
MEHGNet: a multi-feature extraction and high-resolution generative network for satellite cloud image sequence prediction
B Xie, J Dong, C Liu, W Cheng - Earth Science Informatics, 2024 - Springer
Satellite cloud image sequences contain rich spatial and temporal information, and
forecasting future cloud image sequences is of great significance for meteorological …
forecasting future cloud image sequences is of great significance for meteorological …