Change Detection in Dynamic Networks Using Network Characteristics

In recent years, the use of dynamic networks became increasingly popular. An important task is to identify differences at particular time points, e.g. for online monitoring, change-point detection or testing procedures. Due to the complexity of network data, the statistical analysis is challenging. Therefore, it is usually a main step to characterize the networks by one or few scalar-valued metrics at each time point. As the reduction to such metrics can result in information loss, the understanding of their behaviour in various change scenarios is crucial. However, existing studies commonly use specific data examples and do not give any deeper theoretical insights of the general performances. In this paper, we propose a categorization of different types of changes which can occur in network data. We analyze the suitability and limitations of common network metrics in such situations and give comprehensive explanations of their behaviour. This leads to a well-founded advice of which metrics to use in various application scenarios. Our findings are underlined by an extensive simulation study and some real-world data which involve both time-dependent and independent setups for online monitoring.