Signal processing on graphs: Performance of graph structure estimation

A class of models for describing sets of time series generated by interacting agents using directed, weighted graphs is introduced. A computationally tractable algorithm for estimating the graph adjacency matrix of this model from observed time series data is presented. The performance guarantees of this algorithm for prediction are outlined under several assumptions on the properties of the dynamics of the system of agents and on the true values of the parameters. These guarantees are tested empirically through simulation studies using several random graph models.

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