dc.contributor.authorLuo, Wuqiong
dc.contributor.authorTay, Wee Peng
dc.date.accessioned2013-07-31T04:24:45Z
dc.date.available2013-07-31T04:24:45Z
dc.date.copyright2012en_US
dc.date.issued2012
dc.identifier.citationLuo, W., & Tay, W. P. (2012). Identifying infection sources in large tree networks. 2012 9th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON), 281-289.en_US
dc.identifier.urihttp://hdl.handle.net/10220/12595
dc.description.abstractEstimating which nodes in a network are the infection sources, including the individuals who started a rumor in a social network, the computers that introduce a virus into a computer network, or the index cases of a contagious disease, plays a critical role in identifying the influential nodes in a network, and in some applications, limiting the damage caused by the infection through timely quarantine of the sources. We consider the problem of estimating the infection sources, based only on knowledge of the underlying network connections. We derive estimators based on approximations of the infection sequences counts. We show that if there are at most two infection sources in a geometric tree, our estimator identifies these sources with probability going to one as the number of infected nodes increases. When there are more than two infection sources, we present heuristics that have quadratic complexity. We show through simulations that our proposed estimators can correctly identify the infection sources to within a few hops with high probability.en_US
dc.language.isoenen_US
dc.subjectDRNTU::Engineering::Electrical and electronic engineering
dc.titleIdentifying infection sources in large tree networksen_US
dc.typeConference Paper
dc.contributor.conferenceConference on Sensor, Mesh and Ad Hoc Communications and Networks (9th : 2012 : Seoul, Korea)en_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.identifier.doihttp://dx.doi.org/10.1109/SECON.2012.6275788


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