1 code implementation • 25 Mar 2024 • Qinyao Luo, Silu He, Xing Han, YuHan Wang, Haifeng Li
Accurate traffic forecasting is a fundamental problem in intelligent transportation systems and learning long-range traffic representations with key information through spatiotemporal graph neural networks (STGNNs) is a basic assumption of current traffic flow prediction models.
1 code implementation • 14 Dec 2023 • Silu He, Qinyao Luo, Xinsha Fu, Ling Zhao, Ronghua Du, Haifeng Li
To estimate the DE, since the DE are generated through two paths (grab the attention assigned to neighbors and reduce the self-attention of the central node), we use Total Effect to model DE, which is a kind of causal estimand and can be estimated from intervened data; To weaken the DE, we identify the neighbors with the highest DE (we call them Distraction Neighbors) and remove them.
Ranked #1 on Node Classification on Cornell
no code implementations • 30 Oct 2022 • Silu He, Qinyao Luo, Ronghua Du, Ling Zhao, Haifeng Li
We further propose spatial-temporal Granger causality (STGC) to express TCR, which models global and dynamic spatial dependence.
1 code implementation • 15 Oct 2022 • Haifeng Li, Jun Cao, Jiawei Zhu, Qinyao Luo, Silu He, Xuyin Wang
iGCL designs the invariant-discriminative loss (ID loss) to learn invariant and discriminative representations.