Time-varying Graph Learning Based on Sparseness of Temporal Variation

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 problem is formulated as a convex optimization problem to impose a constraint on the temporal relation of the time-varying graph. Experimental results with synthetic data show the effectiveness of our proposed method.