Detecting multiple information sources in networks under the SIR model

Zhen Chen, Kai Zhu, Lei Ying

Research output: Contribution to journalArticle

13 Citations (Scopus)

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)
Article number7395374
Pages (from-to)17-31
Number of pages15
JournalIEEE Transactions on Network Science and Engineering
Volume3
Issue number1
DOIs
StatePublished - Jan 1 2016

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Heuristic algorithms
Clustering algorithms

Keywords

  • Information source detection
  • Multiple information sources
  • Sample path approach

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Control and Systems Engineering

Cite this

Detecting multiple information sources in networks under the SIR model. / Chen, Zhen; Zhu, Kai; Ying, Lei.

In: IEEE Transactions on Network Science and Engineering, Vol. 3, No. 1, 7395374, 01.01.2016, p. 17-31.

Research output: Contribution to journalArticle

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