Nonparametric online monitoring of dynamic networks

Abstract

Network sequence has been commonly used for describing the longitudinal pattern of a dynamic system. Proper online monitoring of a network sequence is thus important for detecting temporal structural changes of the system. To this end, there have been some discussions in the statistical process control (SPC) literature to first extract some features from the observed networks and then apply an SPC chart to monitor the extracted features sequentially over time. However, the features used in many existing methods are insensitive to some important network structural changes, and the control charts used cannot accommodate the complex structure of the extracted features properly. In this paper, we suggest using four specific features to describe the structure of an observed network, and their combination can reflect most network structural changes that we are interested in detecting in various applications. After the four features are extracted from the observed networks, we suggest using a multivariate nonparametric control chart to monitor the extracted features online. Numerical studies show that our proposed network monitoring method is more reliable and effective than some representative existing methods in various cases considered.

Keywords:

• dynamic networks
• features
• hurdle model
• multivariate nonparametric control charts
• network surveillance
• statistical process control

Acknowledgements

The authors thank the editor and two referees for many insightful comments and suggestions, which improved the quality of the paper greatly.

Disclosure statement

No potential conflict of interest was reported by the author(s)

Data Availability Statement

The data that support the findings of this study are available from the authors upon reasonable request.

Additional information

Funding

This work was supported by the Centers for Disease Control and Prevention [U01CK000670]; National Science Foundation [DMS-1914639].

Notes on contributors

Yipeng Wang

Yipeng Wang is a PhD student of the Department of Biostatistics at the University of Florida. He is doing his thesis research under the supervision of Professor Peihua Qiu. He obtained his MS degree from the Department of Statistics at the Florida State University. His major research interests include network description and monitoring, meta analysis, and clinical trial designs.

Xiulin Xie

Xiulin Xie is an assistant professor of the Department of Statistics at the Florida State University. He obtained his PhD degree in 2023 from the Department of Biostatistics at the University of Florida. His major research interests include dynamic process monitoring, environmental surveillance, and machine learning.

Peihua Qiu

Peihua Qiu is Dean’s Professor and Founding Chair of the Department of Biostatistics at the University of Florida. He received his PhD in statistics from the Department of Statistics at the University of Wisconsin at Madison. His major research interests include jump regression analysis, image processing, statistical process control, survival analysis, dynamic disease screening, and spatio-temporal disease surveillance. He is an elected fellow of AAAS, ASA, ASQ and IMS, and elected member of ISI. He was the editor of Technometrics, and the recipient of the 2024 Shewhart Medal.