25 citations · 42 across the 4 of their papers we have counts for
4 papers
Prompt-Based Spatio-Temporal Graph Transfer Learning
Junfeng Hu, Xu Liu, Zhencheng Fan +4
Spatio-temporal graph neural networks have proven efficacy in capturing complex dependencies for urban computing tasks such as forecasting and kriging. Yet, their performance is co…
Towards Unifying Diffusion Models for Probabilistic Spatio-Temporal Graph Learning
Junfeng Hu, Xu Liu, Zhencheng Fan +2
Spatio-temporal graph learning is a fundamental problem in modern urban systems. Existing approaches tackle different tasks independently, tailoring their models to unique task cha…
Graph Neural Processes for Spatio-Temporal Extrapolation
Junfeng Hu, Yuxuan Liang, Zhencheng Fan +3
We study the task of spatio-temporal extrapolation that generates data at target locations from surrounding contexts in a graph. This task is crucial as sensors that collect data a…
Decoupling Long- and Short-Term Patterns in Spatiotemporal Inference
Junfeng Hu, Yuxuan Liang, Zhencheng Fan +3
Sensors are the key to environmental monitoring, which impart benefits to smart cities in many aspects, such as providing real-time air quality information to assist human decision…