An important problem in network analysis is the online detection of anomalous behaviour. In this paper, we introduce a network surveillance method bringing together network modelling and statistical process control. Our approach is to apply multivariate control charts based on exponential smoothing and cumulative sums in order to monitor networks generated by temporal exponential random graph models (TERGM). The latter allows us to account for temporal dependence while simultaneously reducing the number of parameters to be monitored. The performance of the considered charts is evaluated by calculating the average run length and the conditional expected delay for both simulated and real data. To justify the decision of using the TERGM to describe network data, some measures of goodness of fit are inspected. We demonstrate the effectiveness of the proposed approach by an empirical application, monitoring daily flights in the United States to detect anomalous patterns.

网络分析中的一个重要问题是异常行为的在线检测。在本文中，我们介绍了一种将网络建模和统计过程控制结合在一起的网络监视方法。我们的方法是应用基于指数平滑和累积和的多元控制图，以监控由时间指数随机图模型 (TERGM) 生成的网络。后者允许我们考虑时间依赖性，同时减少要监控的参数数量。通过计算模拟和真实数据的平均运行长度和条件预期延迟来评估所考虑图表的性能。为了证明使用 TERGM 描述网络数据的决定的合理性，我们检查了一些拟合优度的度量。