一类相依网络的动态聚类分析

Dynamic Cluster Analysis of Dependent Networks

动态复杂网络因广泛应用于群体生态学、社会生态学、生物学和因特网等领域而成为研究的热门问题之一,聚类分析是提取网络结构的常用工具.以往关于网络聚类的文献大多基于某种特定的条件独立假设.本文我们结合了随机区组模型,隐马尔科夫链和自回归模型给出了一个新模型来放宽这个假设,并给出了相应的统计推断和VEM算法.蒙特卡洛模拟表现良好,表明了我们的方法的一致性和稳健性.

Dynamic complex network has become a popular topic in the many fields, such as population ecology, social ecology, biology and Internet. Meanwhile cluster analysis is a common tool to extract network structure. Previous articles on network clustering mostly supposed that observations are conditionally independent. However, we construct novel model which combines the stochastic block model, the hidden structure in Markov process and the autoregressive model to relax this assumption. We also propose relative statistical inference and VEM algorithm. Finally, the Monte Carlo simulations are performed well, which shows the consistency and robustness of the work.