Statistical learning for change point and anomaly detection in graphs

TL;DR

This paper explores integrating statistical learning and deep learning to improve change point and anomaly detection in dynamic graphs, demonstrated through monitoring ambulance service response times.

Contribution

It proposes a novel approach combining statistical process control with deep learning for enhanced network monitoring in dynamic graph data.

Findings

Effective detection of change points and anomalies in network data.

Successful application to ambulance response time monitoring.

Integration of control charts with graph neural networks.

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 to monitor 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 paper, 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 times of ambulance services, applying jointly the control chart for quantile function values and a graph convolutional network.