8 papers · 1 filter
Diffusing to the Top: Boost Graph Neural Networks with Minimal Hyperparameter Tuning
Lequan Lin, Dai Shi, Andi Han +2
Graph Neural Networks (GNNs) are proficient in graph representation learning and achieve promising performance on versatile tasks such as node classification and link prediction. U…
When Graph Neural Networks Meet Dynamic Mode Decomposition
Dai Shi, Lequan Lin, Andi Han +3
Graph Neural Networks (GNNs) have emerged as fundamental tools for a wide range of prediction tasks on graph-structured data. Recent studies have drawn analogies between GNN featur…
A Riemannian Approach to Ground Metric Learning for Optimal Transport
Pratik Jawanpuria, Dai Shi, Bamdev Mishra +1
Optimal transport (OT) theory has attracted much attention in machine learning and signal processing applications. OT defines a notion of distance between probability distributions…
Machine Learning-Based Prediction of Key Genes Correlated to the Subretinal Lesion Severity in a Mouse Model of Age-Related Macular Degeneration
Kuan Yan, Yue Zeng, Dai Shi +5
Age-related macular degeneration (AMD) is a major cause of blindness in older adults, severely affecting vision and quality of life. Despite advances in understanding AMD, the mole…
SpecSTG: A Fast Spectral Diffusion Framework for Probabilistic Spatio-Temporal Traffic Forecasting
Lequan Lin, Dai Shi, Andi Han +1
Traffic forecasting, a crucial application of spatio-temporal graph (STG) learning, has traditionally relied on deterministic models for accurate point estimations. Yet, these mode…
Quasi-Framelets: Robust Graph Neural Networks via Adaptive Framelet Convolution
Mengxi Yang, Dai Shi, Xuebin Zheng +2
This paper aims to provide a novel design of a multiscale framelet convolution for spectral graph neural networks (GNNs). While current spectral methods excel in various graph lear…