206 citations · 825 across the 18 of their papers we have counts for
10 papers · 1 filter
Deep Learning for Cross-Domain Data Fusion in Urban Computing: Taxonomy, Advances, and Outlook
Xingchen Zou, Yibo Yan, Xixuan Hao +8
As cities continue to burgeon, Urban Computing emerges as a pivotal discipline for sustainable development by harnessing the power of cross-domain data fusion from diverse sources…
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…
Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Survey
Guangyin Jin, Yuxuan Liang, Yuchen Fang +4
With recent advances in sensing technologies, a myriad of spatio-temporal data has been generated and recorded in smart cities. Forecasting the evolution patterns of spatio-tempora…
Spatio-Temporal Self-Supervised Learning for Traffic Flow Prediction
Jiahao Ji, Jingyuan Wang, Chao Huang +5
Robust prediction of citywide traffic flows at different time periods plays a crucial role in intelligent transportation systems. While previous work has made great efforts to mode…
A Cross-City Federated Transfer Learning Framework: A Case Study on Urban Region Profiling
Gaode Chen, Yijun Su, Xinghua Zhang +6
Data insufficiency problems (i.e., data missing and label scarcity) caused by inadequate services and infrastructures or imbalanced development levels of cities have seriously affe…
Traffic Flow Forecasting with Spatial-Temporal Graph Diffusion Network
Xiyue Zhang, Chao Huang, Yong Xu +5
Accurate forecasting of citywide traffic flow has been playing critical role in a variety of spatial-temporal mining applications, such as intelligent traffic control and public ri…
When Do Contrastive Learning Signals Help Spatio-Temporal Graph Forecasting?
Xu Liu, Yuxuan Liang, Chao Huang +3
Deep learning models are modern tools for spatio-temporal graph (STG) forecasting. Though successful, we argue that data scarcity is a key factor limiting their recent improvements…
Federated Extra-Trees with Privacy Preserving
Yang Liu, Mingxin Chen, Wenxi Zhang +2
It is commonly observed that the data are scattered everywhere and difficult to be centralized. The data privacy and security also become a sensitive topic. The laws and regulation…
Federated Forest
Yang Liu, Yingting Liu, Zhijie Liu +3
Most real-world data are scattered across different companies or government organizations, and cannot be easily integrated under data privacy and related regulations such as the Eu…
HyperST-Net: Hypernetworks for Spatio-Temporal Forecasting
Zheyi Pan, Yuxuan Liang, Junbo Zhang +3
Spatio-temporal (ST) data, which represent multiple time series data corresponding to different spatial locations, are ubiquitous in real-world dynamic systems, such as air quality…