Detecting multiple information sources in networks under the SIR model

Zhen Chen, Kai Zhu, Lei Ying

Research output: Chapter in Book/Report/Conference proceedingConference contribution

16 Scopus citations

Abstract

In this paper, we study the problem of detecting multiple information sources in networks under the Susceptible-Infected-Recovered (SIR) model. First, assuming the number of information sources is known, we develop a sample-path-based algorithm, named clustering and localization, for trees. For g-regular trees, the estimators produced by the proposed algorithm are within a constant distance from the real sources with a high probability. We further present a heuristic algorithm for general networks and an algorithm for estimating the number of sources when the number of real sources is unknown.

Original languageEnglish (US)
Title of host publication2014 48th Annual Conference on Information Sciences and Systems, CISS 2014
PublisherIEEE Computer Society
DOIs
StatePublished - 2014
Event2014 48th Annual Conference on Information Sciences and Systems, CISS 2014 - Princeton, NJ, United States
Duration: Mar 19 2014Mar 21 2014

Other

Other2014 48th Annual Conference on Information Sciences and Systems, CISS 2014
CountryUnited States
CityPrinceton, NJ
Period3/19/143/21/14

ASJC Scopus subject areas

  • Information Systems

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  • Cite this

    Chen, Z., Zhu, K., & Ying, L. (2014). Detecting multiple information sources in networks under the SIR model. In 2014 48th Annual Conference on Information Sciences and Systems, CISS 2014 [6814143] IEEE Computer Society. https://doi.org/10.1109/CISS.2014.6814143