Online network monitoring

Malinovskaya, A. and Otto, P. (2021) Online network monitoring. Statistical Methods and Applications, 30, pp. 1337-1364. (doi: 10.1007/s10260-021-00589-z)

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.

• Item Type: Articles
• Additional Information: Open Access funding enabled and organized by Projekt DEAL. Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - Project No. 412992257.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Otto, Dr Philipp
• Authors: Malinovskaya, A., and Otto, P.
• College/School: College of Science and Engineering > School of Mathematics and Statistics > Statistics
• Journal Name: Statistical Methods and Applications
• Publisher: Springer
• ISSN: 1618-2510
• ISSN (Online): 1613-981X
• Copyright Holders: Copyright © The Author(s) 2021
• First Published: First published in Statistical Methods and Applications 30:1337-1364
• Publisher Policy: Reproduced under a Creative Commons license