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    Area of Science:

    • Systems Science
    • Computer Science
    • Control Engineering

    Background:

    • Learning dynamical networks from time series data is crucial.
    • Existing network identification methods primarily address static structures, neglecting dynamic changes.

    Purpose of the Study:

    • To develop a method for identifying the structures of switching dynamical networks.
    • To leverage both temporal and spatial information to characterize network switching processes.

    Main Methods:

    • A new sparse Bayesian learning algorithm utilizing coupled hyperblocks.
    • Estimation of unknown switching instants within the network.

    Main Results:

    • Demonstrated effectiveness in identifying switching network structures.
    • Superior performance compared to existing methods on benchmark datasets.

    Conclusions:

    • The proposed method accurately identifies switching dynamical networks.
    • The approach offers a significant advancement for analyzing time-varying network systems.