Abstract Network monitoring has become increasingly popular in the area of statistical process control due to its wide applications in fraud detection, corporate management and political behavioral analysis. This paper focuses on the cases where the communication pattern is significant while changes in specific nodes are negligible. The important features including the density, reciprocity, degree variability, and transitivity are considered to reflect the commonly-encountered communication patterns in social networks. The structural statistics are provided for characterizing the main features. A multivariate control chart is adopted to monitor the structural statistics simultaneously so as to account for their correlations and to decrease the overall false alarm rate. A performance evaluation framework is proposed based on the Exponential Random Graph Models (ERGMs) in order to simulate the shifts of communication patterns. The results of the numerical experiments show that the Hotelling T2 control chart for the structural statistics outperforms several benchmark methods especially in detecting the large shifts of reciprocity and transitivity. The effectiveness of the proposed method is validated through the analysis of the Enron email communication networks.

中文翻译：

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基于结构统计监测异常通信模式的二进制网络

摘要 网络监控因其在欺诈检测、企业管理和政治行为分析中的广泛应用而在统计过程控制领域越来越受欢迎。本文重点讨论通信模式显着而特定节点的变化可以忽略不计的情况。包括密度、互惠性、程度可变性和传递性在内的重要特征被认为反映了社交网络中常见的通信模式。提供结构统计以表征主要特征。采用多变量控制图同时监测结构统计，以考虑它们的相关性并降低整体误报率。为了模拟通信模式的变化，提出了一种基于指数随机图模型（ERGM）的性能评估框架。数值实验的结果表明，用于结构统计的 Hotelling T2 控制图优于几种基准方法，尤其是在检测互易性和传递性的大偏移方面。通过对安然电子邮件通信网络的分析，验证了所提出方法的有效性。