Search Results for author:
Found 4 papers, 3 papers with code
LSTTN: A Long-Short Term Transformer-based Spatio-temporal Neural Network for Traffic Flow Forecasting
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.
CAT: A Causally Graph Attention Network for Trimming Heterophilic Graph
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
STGC-GNNs: A GNN-based traffic prediction framework with a spatial-temporal Granger causality graph
We further propose spatial-temporal Granger causality (STGC) to express TCR, which models global and dynamic spatial dependence.
Augmentation-Free Graph Contrastive Learning of Invariant-Discriminative Representations
iGCL designs the invariant-discriminative loss (ID loss) to learn invariant and discriminative representations.
