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This paper studies the change point detection problem in time series ofnetworks, with the Separable Temporal Exponential-family Random Graph Model(STERGM). We consider a sequence of networks generated from a piecewiseconstant distribution that is altered at unknown change points in time.Detection of the change points can identify the discrepancies in the underlyingdata generating processes and facilitate downstream dynamic network analysis.Moreover, the STERGM that focuses on network statistics is a flexible model tofit dynamic networks with both dyadic and temporal dependence. We propose a newestimator derived from the Alternating Direction Method of Multipliers (ADMM)and the Group Fused Lasso to simultaneously detect multiple time points, wherethe parameters of STERGM have changed. We also provide a Bayesian informationcriterion for model selection to assist the detection. Our experiments showgood performance of the proposed method on both simulated and real data.Lastly, we develop an R package CPDstergm to implement our method.

Change Point Detection on A Separable Model for Dynamic Networks