This paper studies change point detection in time series of networks, with the Separable Temporal Exponential-family Random Graph Model (STERGM). Dynamic network patterns can be inherently complex due to dyadic and temporal dependence. Detection of the change points can identify the discrepancies in the underlying data generating processes and facilitate downstream analysis. The STERGM that utilizes network statistics to represent the structural patterns is a flexible model to fit dynamic networks. We propose a new estimator derived from the Alternating Direction Method of Multipliers (ADMM) and Group Fused Lasso to simultaneously detect multiple time points, where the parameters of a time-heterogeneous STERGM have changed. We also provide a Bayesian information criterion for model selection and an R package CPDstergm to implement the proposed method. Experiments on simulated and real data show good performance of the proposed framework.

翻译：本文研究网络时间序列中的变点检测问题，采用可分离时间指数族随机图模型（STERGM）。由于二元关系和时序依赖性的存在，动态网络模式本质上具有复杂性。变点检测能够识别底层数据生成过程中的差异，并促进下游分析。STERGM利用网络统计量表示结构模式，是拟合动态网络的灵活模型。我们提出一种基于交替方向乘子法（ADMM）与组融合Lasso的新估计量，用于同时检测时间异质性STERGM参数发生变化的多个时间点。同时提供了用于模型选择的贝叶斯信息准则，以及实现所提方法的R包CPDstergm。在模拟数据和真实数据上的实验表明，所提框架具有良好的性能。