Statistical learning for change point and anomaly detection in graphs

Malinovskaya, A., Otto, P. and Peters, T. (2022) Statistical learning for change point and anomaly detection in graphs. In: Steland, A. and Tsui, K.-L. (eds.) Artificial Intelligence, Big Data and Data Science in Statistics. Springer, pp. 85-109. ISBN 9783031071553 (doi: 10.1007/978-3-031-07155-3_4)

Abstract

Complex systems which can be represented in the form of static and dynamic graphs arise in different fields, e.g., communication, engineering and industry. One of the interesting problems in analysing dynamic network structures is monitoring changes in their development. Statistical learning, which encompasses both methods based on artificial intelligence and traditional statistics, can be used to progress in this research area. However, the majority of approaches apply only one or the other framework. In this chapter, we discuss the possibility of bringing together both disciplines in order to create enhanced network monitoring procedures focussing on the example of combining statistical process control and deep learning algorithms. Together with the presentation of change point and anomaly detection in network data, we propose to monitor the response time of ambulance service, applying jointly the control chart for quantile function values and a graph convolutional network.

• Item Type: Book Sections
• Additional Information: Print ISBN: 9783031071546.
• Status: Published
• Glasgow Author(s) Enlighten ID: Otto, Dr Philipp
• Authors: Malinovskaya, A., Otto, P., and Peters, T.
• College/School: College of Science and Engineering > School of Mathematics and Statistics > Statistics
• Publisher: Springer
• ISBN: 9783031071553
• Published Online: 15 November 2022

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