Online network monitoring

Anna Malinovskaya () and Philipp Otto ()
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Anna Malinovskaya: Leibniz University Hannover
Philipp Otto: Leibniz University Hannover

Statistical Methods & Applications, 2021, vol. 30, issue 5, No 4, 1337-1364

Abstract: Abstract 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.

Keywords: MCUSUM; MEWMA; Multivariate Control Charts; Network Modelling; Network Monitoring; Statistical Process Control; TERGM (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s10260-021-00589-z

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