Time-Varying Graph Learning with Constraints on Graph Temporal Variation
arXiv:2001.03346 · doi:10.1109/TSP.2026.3712068
Abstract
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 three regularization terms in convex optimization problems that constrain the sparseness of temporal variations of the time-varying networks. Moreover, a computationally scalable algorithm is introduced to solve the optimization problem efficiently. The experimental results with synthetic and real datasets (point cloud, temperature, and EEG data) demonstrate that our proposed method outperforms state-of-the-art methods.
Accepted for publication in IEEE Transactions on Signal Processing. Copyright 2026 IEEE. Personal use of this material is permitted
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