Statistical evaluation of spectral methods for anomaly detection in static networks. (September 2019)

• Record Type: Journal Article
• Authors: Komolafe, Tomilayo; Quevedo, A. Valeria; Sengupta, Srijan; Woodall, William H.
• Is Part Of: Network science. Volume 7:Number 3(2019)
• Page Start: 319
• Page End: 352
• Publication Date: 2019-09
• Subjects: residual matrix, -- spectral methods, -- R-MAT model, -- principal components; Social networks -- Research -- Periodicals; System analysis -- Periodicals; System theory -- Periodicals; Computer science -- Periodicals; 003.72
• DOI: 10.1017/nws.2019.14
• Languages: English
• ISSNs: 2050-1242

Abstract

Abstract: The topic of anomaly detection in networks has attracted a lot of attention in recent years, especially with the rise of connected devices and social networks. Anomaly detection spans a wide range of applications, from detecting terrorist cells in counter-terrorism efforts to identifying unexpected mutations during ribonucleic acid transcription. Fittingly, numerous algorithmic techniques for anomaly detection have been introduced. However, to date, little work has been done to evaluate these algorithms from a statistical perspective. This work is aimed at addressing this gap in the literature by carrying out statistical evaluation of a suite of popular spectral methods for anomaly detection in networks. Our investigation on the statistical properties of these algorithms reveals several important and critical shortcomings that we make methodological improvements to address. Further, we carry out a performance evaluation of these algorithms using simulated networks and extend the methods from binary to count networks.