Anomaly detection in dynamic networks: a survey

Autor:	Stephen Ranshous, Shitian Shen, Steve Harenberg, Danai Koutra, Christos Faloutsos, Nagiza F. Samatova
Rok vydání:	2015
Předmět:	Statistics and Probability Computer science Multiple applications Snapshot (computer storage) Anomaly detection Data mining computer.software_genre computer
Zdroj:	Wiley Interdisciplinary Reviews: Computational Statistics. 7:223-247
ISSN:	1939-5108
DOI:	10.1002/wics.1347
Popis:	Anomaly detection is an important problem with multiple applications, and thus has been studied for decades in various research domains. In the past decade there has been a growing interest in anomaly detection in data represented as networks, or graphs, largely because of their robust expressiveness and their natural ability to represent complex relationships. Originally, techniques focused on anomaly detection in static graphs, which do not change and are capable of representing only a single snapshot of data. As real-world networks are constantly changing, there has been a shift in focus to dynamic graphs, which evolve over time.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::b2ee7d7413e75f07a49bcb9384fae342 https://doi.org/10.1002/wics.1347 Zobrazit plný text záznamu Plný text ve formátu PDF Plný text ve formátu HTML
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