Abstract
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 traf-
fic network datasets, METR-LA and PEMS-BAY,
demonstrate the superior performance of our algorithm