2025
Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph Learning
ICASSP 2025accepted
Dynamic graph augmentation is used to improve the performance of dynamic GNNs. Most methods assume temporal locality, meaning that recent edges are more influential than earlier edges. However, for temporal changes in edges caused by random noise, overemphasizing recent edges while neglecting earlie…