UQGNN couples diffusion graph convolution, temporal convolution, and a multivariate Gaussian output head to jointly predict heterogeneous urban phenomena with uncertainty, and it outperforms twelve baselines across four city datasets.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
UQGNN: Uncertainty Quantification of Graph Neural Networks for Multivariate Spatiotemporal Prediction
UQGNN couples diffusion graph convolution, temporal convolution, and a multivariate Gaussian output head to jointly predict heterogeneous urban phenomena with uncertainty, and it outperforms twelve baselines across four city datasets.