2 papers
cs.LG2025
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…
cs.AI2024
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…