Abstract Network monitoring is a new area in statistical process control applications. It aims at detecting assignable changes in the communication structure of a network. The probability of communications in social networks is usually based on the attributes of vertices. Moreover, due to the nature of human relationships, social networks are almost time-dependent. Neglecting this feature in control chart design reduces the chart performance. In this paper, communications are defined as autoregressive binary variables with the probability modeled by the logit link function. The explanatory variables of the model are the vertices’ attributes and previous information of the network. Accordingly, we propose three likelihood ratio test-based methods, one static and two dynamic reference methods. The performance of the proposed methods is evaluated using simulation studies and real numerical examples from the email communications of Enron Corporation. Then, the effect of the autocorrelation structure on the link function is investigated. Also, the effect of parameter estimation on the ARL measure of the proposed methods is studied. Furthermore, the performance of the proposed methods is compared with three traditional methods. Finally, some practical suggestions are given for different out-of-control situations and statistical designs.

中文翻译：

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基于似然统计监测自回归二元社交网络

摘要 网络监控是统计过程控制应用中的一个新领域。它旨在检测网络通信结构中可分配的变化。社交网络中通信的概率通常基于顶点的属性。此外，由于人际关系的性质，社交网络几乎是依赖于时间的。在控制图设计中忽略此功能会降低图表性能。在本文中，通信被定义为自回归二元变量，其概率由 logit 链接函数建模。模型的解释变量是顶点的属性和网络的先前信息。因此，我们提出了三种基于似然比测试的方法，一种静态参考方法和两种动态参考方法。使用模拟研究和来自安然公司电子邮件通信的真实数值示例来评估所提出方法的性能。然后，研究了自相关结构对链接函数的影响。此外，还研究了参数估计对所提出方法的 ARL 度量的影响。此外，将所提出方法的性能与三种传统方法进行了比较。最后，针对不同的失控情况和统计​​设计给出了一些实用的建议。将所提出方法的性能与三种传统方法进行了比较。最后，针对不同的失控情况和统计​​设计给出了一些实用的建议。将所提出方法的性能与三种传统方法进行了比较。最后，针对不同的失控情况和统计​​设计给出了一些实用的建议。