Details
| Original language | English |
|---|---|
| Title of host publication | Artificial Intelligence, Big Data and Data Science in Statistics |
| Subtitle of host publication | Challenges and Solutions in Environmetrics, the Natural Sciences and Technology |
| Editors | Ansgar Steland, Kwok-Leung Tsui |
| Pages | 85-110 |
| Number of pages | 26 |
| Edition | 1 |
| ISBN (electronic) | 978-3-031-07155-3 |
| Publication status | Published - 16 Nov 2022 |
Abstract
Keywords
- Control charts, Graph convolutional networks, Machine learning on graphs, Network monitoring, Neural networks, Statistical process control
ASJC Scopus subject areas
- Mathematics(all)
- General Mathematics
- Computer Science(all)
- General Computer Science
Cite this
- Standard
- Harvard
- Apa
- Vancouver
- BibTeX
- RIS
Artificial Intelligence, Big Data and Data Science in Statistics: Challenges and Solutions in Environmetrics, the Natural Sciences and Technology. ed. / Ansgar Steland; Kwok-Leung Tsui. 1. ed. 2022. p. 85-110.
Research output: Chapter in book/report/conference proceeding › Contribution to book/anthology › Research › peer review
}
TY - CHAP
T1 - Statistical learning for change point and anomaly detection in graphs
AU - Malinovskaya, A.
AU - Otto, P.
AU - Peters, T.
N1 - Funding Information: This research is supported by the German Research Foundation within the project 412992257.
PY - 2022/11/16
Y1 - 2022/11/16
N2 - 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.
AB - 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.
KW - Control charts
KW - Graph convolutional networks
KW - Machine learning on graphs
KW - Network monitoring
KW - Neural networks
KW - Statistical process control
UR - http://www.scopus.com/inward/record.url?scp=85161579311&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2011.06080
DO - 10.48550/arXiv.2011.06080
M3 - Contribution to book/anthology
SN - 978-3-031-07157-7
SN - 978-3-031-07154-6
SP - 85
EP - 110
BT - Artificial Intelligence, Big Data and Data Science in Statistics
A2 - Steland, Ansgar
A2 - Tsui, Kwok-Leung
ER -