64 citations · 76 across the 16 of their papers we have counts for
10 papers · 1 filter
Efficient Prompt Learning for Traffic Forecasting
Qianru Zhang, Xinyi Gao, Alexander Zhou +3
Accurate traffic prediction is essential for optimizing transportation systems, enhancing resource allocation, and improving overall urban administration. Spatio-temporal graph neu…
FLDmamba: Integrating Fourier and Laplace Transform Decomposition with Mamba for Enhanced Time Series Prediction
Qianru Zhang, Chenglei Yu, Haixin Wang +5
Time series prediction, a crucial task across various domains, faces significant challenges due to the inherent complexities of time series data, including non-stationarity, multi-…
AutoHFormer: Efficient Hierarchical Autoregressive Transformer for Time Series Prediction
Qianru Zhang, Honggang Wen, Ming Li +4
Time series forecasting requires architectures that simultaneously achieve three competing objectives: (1) strict temporal causality for reliable predictions, (2) sub-quadratic com…
Efficient Traffic Prediction Through Spatio-Temporal Distillation
Qianru Zhang, Xinyi Gao, Haixin Wang +2
Graph neural networks (GNNs) have gained considerable attention in recent years for traffic flow prediction due to their ability to learn spatio-temporal pattern representations th…
HGAurban: Heterogeneous Graph Autoencoding for Urban Spatial-Temporal Learning
Qianru Zhang, Xinyi Gao, Haixin Wang +3
Spatial-temporal graph representations play a crucial role in urban sensing applications, including traffic analysis, human mobility behavior modeling, and citywide crime predictio…
A Survey of Generative Techniques for Spatial-Temporal Data Mining
Qianru Zhang, Haixin Wang, Cheng Long +8
This paper focuses on the integration of generative techniques into spatial-temporal data mining, considering the significant growth and diverse nature of spatial-temporal data. Wi…
Graph Augmentation for Recommendation
Qianru Zhang, Lianghao Xia, Xuheng Cai +3
Graph augmentation with contrastive learning has gained significant attention in the field of recommendation systems due to its ability to learn expressive user representations, ev…
Spatial-Temporal Graph Learning with Adversarial Contrastive Adaptation
Qianru Zhang, Chao Huang, Lianghao Xia +3
Spatial-temporal graph learning has emerged as a promising solution for modeling structured spatial-temporal data and learning region representations for various urban sensing task…
Automated Spatio-Temporal Graph Contrastive Learning
Qianru Zhang, Chao Huang, Lianghao Xia +3
Among various region embedding methods, graph-based region relation learning models stand out, owing to their strong structure representation ability for encoding spatial correlati…
On Inferring User Socioeconomic Status with Mobility Records
Zheng Wang, Mingrui Liu, Cheng Long +3
When users move in a physical space (e.g., an urban space), they would have some records called mobility records (e.g., trajectories) generated by devices such as mobile phones and…