This paper studies the unsupervised change point detection problem in time series of networks using the Separable Temporal Exponential-family Random Graph Model (STERGM). Inherently, dynamic network patterns can be complex due to dyadic and temporal dependence, and change points detection can identify the discrepancies in the underlying data generating processes to facilitate downstream analysis. Moreover, the STERGM that utilizes network statistics to represent the structural patterns is a flexible and parsimonious model to fit dynamic networks. We propose a new estimator derived from the Alternating Direction Method of Multipliers (ADMM) procedure and Group Fused Lasso (GFL) regularization 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：本文研究基于可分离时序指数族随机图模型(STERGM)的网络时间序列无监督变点检测问题。动态网络模式本质上可能因二元依赖与时间依赖性而变得复杂，变点检测能够识别底层数据生成过程的差异，从而促进下游分析。此外，利用网络统计量表征结构模式的STERGM是拟合动态网络的灵活且简约的模型。我们提出一种基于交替方向乘子法(ADMM)与组融合套索(GFL)正则化的新估计器，用于同步检测时间异质STERGM参数发生变化的多个时间点。同时提供了模型选择的贝叶斯信息准则及实现该方法的R软件包CPDstergm。在模拟数据与真实数据上的实验表明，所提框架具有良好的性能。