136 citations · 316 across the 6 of their papers we have counts for
6 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…
STGC-GNNs: A GNN-based traffic prediction framework with a spatial-temporal Granger causality graph
Silu He, Qinyao Luo, Ronghua Du +2
The key to traffic prediction is to accurately depict the temporal dynamics of traffic flow traveling in a road network, so it is important to model the spatial dependence of the r…
Augmentation-Free Graph Contrastive Learning of Invariant-Discriminative Representations
Haifeng Li, Jun Cao, Jiawei Zhu +3
The pretasks are mainly built on mutual information estimation, which requires data augmentation to construct positive samples with similar semantics to learn invariant signals and…