13 citations · 30 across the 7 of their papers we have counts for
5 papers · 1 filter
Graph Navier Stokes Networks
Zexing Zhao, Guangsi Shi, Yu Gong +4
Graph Neural Networks (GNNs) have emerged as a cornerstone of deep learning, with most existing methods rooted in graph signal processing and diffusion equations to model message p…
Contrastive Dual-Interaction Graph Neural Network for Molecular Property Prediction
Zexing Zhao, Guangsi Shi, Xiaopeng Wu +3
Molecular property prediction is a key component of AI-driven drug discovery and molecular characterization learning. Despite recent advances, existing methods still face challenge…
Towards Complex Dynamic Physics System Simulation with Graph Neural ODEs
Guangsi Shi, Daokun Zhang, Ming Jin +2
The great learning ability of deep learning models facilitates us to comprehend the real physical world, making learning to simulate complicated particle systems a promising endeav…
Towards Expressive Spectral-Temporal Graph Neural Networks for Time Series Forecasting
Ming Jin, Guangsi Shi, Yuan-Fang Li +7
Time series forecasting has remained a focal point due to its vital applications in sectors such as energy management and transportation planning. Spectral-temporal graph neural ne…
Mitigating Data Redundancy to Revitalize Transformer-based Long-Term Time Series Forecasting System
Mingjie Li, Rui Liu, Guangsi Shi +5
Long-term time-series forecasting (LTSF) is fundamental to various real-world applications, where Transformer-based models have become the dominant framework due to their ability t…