DGAE is a new inductive graph model using directed DEFP, latent encoding, and physics-guided pattern-specific propagation to outperform prior methods on sparse-sensor freeway traffic estimation.
Available: https://arxiv.org/abs/2311.02565
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AdaKernel learns adaptive kernel scale parameters inside GNNs for spatiotemporal data while preserving geometric structure, with experiments showing gains on kriging, imputation and forecasting tasks.
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Network-wide Freeway Traffic Estimation Using Sparse Sensor Data: A Dirichlet Graph Auto-Encoder Approach
DGAE is a new inductive graph model using directed DEFP, latent encoding, and physics-guided pattern-specific propagation to outperform prior methods on sparse-sensor freeway traffic estimation.
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AdaKernel: Learning Adaptive Kernel Parameters for Spatiotemporal Graph Neural Networks
AdaKernel learns adaptive kernel scale parameters inside GNNs for spatiotemporal data while preserving geometric structure, with experiments showing gains on kriging, imputation and forecasting tasks.