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Identifying dependencies among variables in a complex system is an importantproblem in network science. Structural equation models (SEM) have been usedwidely in many fields for topology inference, because they are tractable andincorporate exogenous influences in the model. Topology identification based onstatic SEM is useful in stationary environments; however, in many applicationsa time-varying underlying topology is sought. This paper presents an onlinealgorithm to track sparse time-varying topologies in dynamic environments andmost importantly, performs a detailed analysis on the performance guarantees.The tracking capability is characterized in terms of a bound on the dynamicregret of the proposed algorithm. Numerical tests show that the proposedalgorithm can track changes under different models of time-varying topologies.

Dynamic Regret Analysis for Online Tracking of Time-varying Structural Equation Model Topologies