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Found 4 papers, 3 papers with code
INCREASE: Inductive Graph Representation Learning for Spatio-Temporal Kriging
It aims to infer knowledge for (the things at) unobserved locations using the data from (the things at) observed locations during a given time period of interest.
Multi-Graph Fusion Networks for Urban Region Embedding
Human mobility data contains rich but abundant information, which yields to the comprehensive region embeddings for cross domain tasks.
Spatio-Temporal Joint Graph Convolutional Networks for Traffic Forecasting
However, this approach failed to explicitly reflect the correlations between different nodes at different time steps, thus limiting the learning capability of graph neural networks.
GMAN: A Graph Multi-Attention Network for Traffic Prediction
Between the encoder and the decoder, a transform attention layer is applied to convert the encoded traffic features to generate the sequence representations of future time steps as the input of the decoder.
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