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