  Spatial-temporal graph modeling is an important task to analyze the spatial
relations and temporal trends of components in a system. Existing approaches
mostly capture the spatial dependency on a fixed graph structure, assuming that
the underlying relation between entities is pre-determined. However, the
explicit graph structure (relation) does not necessarily reflect the true
dependency and genuine relation may be missing due to the incomplete
connections in the data. Furthermore, existing methods are ineffective to
capture the temporal trends as the RNNs or CNNs employed in these methods
cannot capture long-range temporal sequences. To overcome these limitations, we
propose in this paper a novel graph neural network architecture, Graph WaveNet,
for spatial-temporal graph modeling. By developing a novel adaptive dependency
matrix and learn it through node embedding, our model can precisely capture the
hidden spatial dependency in the data. With a stacked dilated 1D convolution
component whose receptive field grows exponentially as the number of layers
increases, Graph WaveNet is able to handle very long sequences. These two
components are integrated seamlessly in a unified framework and the whole
framework is learned in an end-to-end manner. Experimental results on two
public traffic network datasets, METR-LA and PEMS-BAY, demonstrate the superior
performance of our algorithm.
