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Statistical Learning for Change Point and Anomaly Detection in Graphs

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Artificial Intelligence, Big Data and Data Science in Statistics

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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.

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Acknowledgements

This research is supported by the German Research Foundation within the project 412992257. The authors thank Artem Leichter and Thomas Cope for valuable insight and fruitful discussions.

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Authors and Affiliations

  1. Leibniz University Hannover, Institute of Cartography and Geoinformatics, Hannover, Germany

    Anna Malinovskaya & Philipp Otto

  2. ETH Zürich, Institute of Geodesy and Photogrammetry, Zürich, Switzerland

    Torben Peters

Authors
  1. Anna Malinovskaya
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  2. Philipp Otto
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  3. Torben Peters
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Corresponding authors

Correspondence to Philipp Otto or Torben Peters .

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Editors and Affiliations

  1. Institute of Statistics and AI Center, RWTH Aachen University, Aachen, Germany

    Ansgar Steland

  2. Grado Department of Industrial and Systems Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA

    Kwok-Leung Tsui

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Malinovskaya, A., Otto, P., Peters, T. (2022). Statistical Learning for Change Point and Anomaly Detection in Graphs. In: Steland, A., Tsui, KL. (eds) Artificial Intelligence, Big Data and Data Science in Statistics. Springer, Cham. https://doi.org/10.1007/978-3-031-07155-3_4

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