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....

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Bibliographic Details
Published in:IEEE transactions on signal and information processing over networks 2021, Vol.7, p.451-464
Main Authors: Flossdorf, Jonathan, Jentsch, Carsten
Format: Article
Language:English
Subjects:
Change detection
Change-point detection
Complexity theory
Control charts
Data analysis
Data models
dynamic network
Measurement
Monitoring
network analysis
network characteristics
Networks
online monitoring
Statistical analysis
temporal network
Testing
Time dependence
Time series analysis
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Summary: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.
ISSN:2373-776X
2373-776X
2373-7778
DOI:10.1109/TSIPN.2021.3094900