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Statistical Learning for Change Point and Anomaly Detection in Graphs

In: Artificial Intelligence, Big Data and Data Science in Statistics

Author

Listed:
  • 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)

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.

Suggested Citation

  • Anna Malinovskaya & Philipp Otto & Torben Peters, 2022. "Statistical Learning for Change Point and Anomaly Detection in Graphs," Springer Books, in: Ansgar Steland & Kwok-Leung Tsui (ed.), Artificial Intelligence, Big Data and Data Science in Statistics, pages 85-109, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-07155-3_4
    DOI: 10.1007/978-3-031-07155-3_4
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