136 citations · 172 across the 4 of their papers we have counts for
4 papers
Causal invariant geographic network representations with feature and structural distribution shifts
Yuhan Wang, Silu He, Qinyao Luo +4
The existing methods learn geographic network representations through deep graph neural networks (GNNs) based on the i.i.d. assumption. However, the spatial heterogeneity and tempo…
STDCformer: A Transformer-Based Model with a Spatial-Temporal Causal De-Confounding Strategy for Crowd Flow Prediction
Silu He, Peng Shen, Pingzhen Xu +2
Existing works typically treat spatial-temporal prediction as the task of learning a function to transform historical observations to future observations. We further decompose…
LSTTN: A Long-Short Term Transformer-based Spatio-temporal Neural Network for Traffic Flow Forecasting
Qinyao Luo, Silu He, Xing Han +2
Accurate traffic forecasting is a fundamental problem in intelligent transportation systems and learning long-range traffic representations with key information through spatiotempo…
CAT: A Causally Graph Attention Network for Trimming Heterophilic Graph
Silu He, Qinyao Luo, Xinsha Fu +3
Local Attention-guided Message Passing Mechanism (LAMP) adopted in Graph Attention Networks (GATs) is designed to adaptively learn the importance of neighboring nodes for better lo…