Statistical Learning for Change Point and Anomaly Detection in Graphs

Anna Malinovskaya (), Philipp Otto () and Torben Peters ()
Additional contact information
Anna Malinovskaya: Institute of Cartography and Geoinformatics, Leibniz University Hannover
Philipp Otto: Institute of Cartography and Geoinformatics, Leibniz University Hannover
Torben Peters: Institute of Geodesy and Photogrammetry, ETH Zürich

A chapter in Artificial Intelligence, Big Data and Data Science in Statistics, 2022, pp 85-109 from Springer

Abstract: 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.

Keywords: Network monitoring; Statistical process control; Control charts; Neural networks; Machine learning on graphs; Graph convolutional networks (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-07155-3_4

Ordering information: This item can be ordered from
http://www.springer.com/9783031071553

DOI: 10.1007/978-3-031-07155-3_4

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().