Scalable Detection of Anomalous Patterns With Connectivity Constraints

We present GraphScan, a novel method for detecting arbitrarily shaped connected clusters in graph or network data. Given a graph structure, data observed at each node, and a score function defining the anomalousness of a set of nodes, GraphScan can efficiently and exactly identify the most anomalous...

Full description

Bibliographic Details

Published in:	Journal of computational and graphical statistics Vol. 24; no. 4; pp. 1014 - 1033
Main Authors:	Speakman, Skyler, McFowland, Edward, Neill, Daniel B.
Format:	Journal Article
Language:	English
Published:	Alexandria Taylor & Francis 02.10.2015 American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America Taylor & Francis Ltd
Subjects:	Biosurveillance Cluster analysis Clustering and Pattern Detection Epidemics Event detection Graph mining Graph theory Public health Scan statistics Sensors Simulation Spatial scan statistic Studies
ISSN:	1061-8600, 1537-2715
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!