Statistical Clustering of Temporal Networks Through a Dynamic Stochastic Block Model
基于动态随机块模型的时变网络统计聚类
Summary Statistical node clustering in discrete time dynamic networks is an emerging field that raises many challenges. Here, we explore statistical properties and frequentist inference in a model that combines a stochastic block model for its static part with independent Markov chains for the evolution of the nodes groups through time. We model binary data as well as weighted dynamic random graphs (with discrete or continuous edges values). Our approach, motivated by the…