We propose a method for graph learning from spatiotemporal measurements. We aim at inferring time-varying graphs under the assumption that changes in graph topology and weights are sparse in time. The ...

This method employs Gated Recurrent Unit to extract the temporal correlation and Graph Convolutional Network to extract the traffic network’s spatial structure. In consideration of the varying ...