most citedSpatio-Temporal Fluid Dynamics Modeling via Physical-Awareness and Parameter Diffusion Guidance

3 citations · 3 across the 1 of their papers we have counts for

collaborators

6 papers

cs.MA2024

NetSafe: Exploring the Topological Safety of Multi-agent Networks

Miao Yu, Shilong Wang, Guibin Zhang +6

Large language models (LLMs) have empowered nodes within multi-agent networks with intelligence, showing growing applications in both academia and industry. However, how to prevent…

cs.CV2024

Causal Deciphering and Inpainting in Spatio-Temporal Dynamics via Diffusion Model

Yifan Duan, Jian Zhao, pengcheng +8

Spatio-temporal (ST) prediction has garnered a De facto attention in earth sciences, such as meteorological prediction, human mobility perception. However, the scarcity of data cou…

cs.LG2024

ForecastGrapher: Redefining Multivariate Time Series Forecasting with Graph Neural Networks

Wanlin Cai, Kun Wang, Hao Wu +2

The challenge of effectively learning inter-series correlations for multivariate time series forecasting remains a substantial and unresolved problem. Traditional deep learning mod…

cs.LG2024

All Nodes are created Not Equal: Node-Specific Layer Aggregation and Filtration for GNN

Shilong Wang, Hao Wu, Yifan Duan +6

The ever-designed Graph Neural Networks, though opening a promising path for the modeling of the graph-structure data, unfortunately introduce two daunting obstacles to their deplo…

cs.LG20243 cited

Spatio-Temporal Fluid Dynamics Modeling via Physical-Awareness and Parameter Diffusion Guidance

Hao Wu, Fan Xu, Yifan Duan +6

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 simulation and p…

cs.LG2024

CaT-GNN: Enhancing Credit Card Fraud Detection via Causal Temporal Graph Neural Networks

Yifan Duan, Guibin Zhang, Shilong Wang +6

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…