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Spatially Focused Attack against Spatiotemporal Graph Neural Networks
[article]
2021
arXiv
pre-print
Spatiotemporal forecasting plays an essential role in various applications in intelligent transportation systems (ITS), such as route planning, navigation, and traffic control and management. Deep Spatiotemporal graph neural networks (GNNs), which capture both spatial and temporal patterns, have achieved great success in traffic forecasting applications. Understanding how GNNs-based forecasting work and the vulnerability and robustness of these models becomes critical to real-world
arXiv:2109.04608v1
fatcat:c6jliz44fnaxhh77qk3acb3kbm