Scan Statistics on Graphs and Networks

Panpan Zhang () and Joseph Glaz ()
Additional contact information
Panpan Zhang: University of Connecticut, Department of Statistics
Joseph Glaz: University of Connecticut, Department of Statistics

Chapter 25 in Handbook of Scan Statistics, 2024, pp 507-542 from Springer

Abstract: Abstract This article summarizes modern research of scan statistics on graphs and networks. These statistics arise naturally in the scanning of time and space looking for clusters of anomalous entities or events. We review theories and methodologies of constructing scan statistics for both static and dynamic graphs, in both purely spatial and spatio temporal frameworks. Computation of graph-structured scan statistics is challenging, and usually leads to NP-hard problems. We also review several popular convex approximation algorithms for computing scan statistics in this article.

Keywords: Anomaly detection; Convex approximation; Random geometric graphs; Scan statistics; Static networks; Time series of graphs (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-1-4614-8033-4_43

Ordering information: This item can be ordered from
http://www.springer.com/9781461480334

DOI: 10.1007/978-1-4614-8033-4_43

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().