A path algorithm for localizing anomalous activity in graphs

The localization of anomalous activity in graphs is a statistical problem that arises in many applications, such as network surveillance, disease outbreak detection, and activity monitoring in social networks. We will address the localization of a cluster of activity in Gaussian noise in directed, w...

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Bibliographic Details
Published in2013 IEEE Global Conference on Signal and Information Processing (GlobalSIP) pp. 341 - 344
Main Author Sharpnack, James
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2013
Subjects
Capacitance
Computational modeling
Computer vision
Kernel
Markov processes
Social network services
Surveillance
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DOI10.1109/GlobalSIP.2013.6736885

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Summary:The localization of anomalous activity in graphs is a statistical problem that arises in many applications, such as network surveillance, disease outbreak detection, and activity monitoring in social networks. We will address the localization of a cluster of activity in Gaussian noise in directed, weighted graphs. We develop a penalized likelihood estimator (we call the relaxed graph scan) as a relaxation of the NP-hard graph scan statistic. We review how the relaxed graph scan (RGS) can be solved using graph cuts, and outline the max-flow min-cut duality. We use this combinatorial duality to derive a path algorithm for the RGS by solving successive max flows. We demonstrate the effectiveness of the RGS on two simulations, over an undirected and directed graph.
DOI:10.1109/GlobalSIP.2013.6736885