  We propose a novel framework for learning time-varying graphs from
spatiotemporal measurements. Given an appropriate prior on the temporal
behavior of signals, our proposed method can estimate time-varying graphs from
a small number of available measurements. To achieve this, we introduce two
regularization terms in convex optimization problems that constrain sparseness
of temporal variations of the time-varying networks. Moreover, a
computationally-scalable algorithm is introduced to efficiently solve the
optimization problem. The experimental results with synthetic and real datasets
(point cloud and temperature data) demonstrate our proposed method outperforms
the existing state-of-the-art methods.
