Recovery of Time Series of Graph Signals Over Dynamic Topology

Conventional studies on time-varying graph signal recovery involve leveraging priors of both temporal and vertex domains for effective estimations. However, these methods all assume a static graph, in spite of the time-varying signals. We believe that such assumption, a static graph signal model, is insufficient to represent some cases where the underlying graph is explicitly dynamic. In this paper, we propose a novel recovering framework for dynamic graph signal models that leverage both temporal and vertex-domain priors. To achieve this, we introduce regularization terms in a convex optimization problem that capture behaviors of graph signals in the two domains, respectively, and integrate the dynamics of the dynamic graph topology into the formulation. We compare the proposed framework to the conventional framework through experiments on synthetic datasets to show the advantageous results of our method in numerous settings.