Time-Varying Graph Learning with Constraints on Graph Temporal Variation
This work addresses the challenge of estimating dynamic networks from sparse data, which is incremental as it builds on prior graph learning methods by adding temporal constraints.
The authors tackled the problem of learning time-varying graphs from limited spatiotemporal measurements by introducing a novel framework with regularization terms that constrain the sparseness of temporal variations, and their method outperformed existing state-of-the-art methods in experiments with synthetic and real datasets.
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.