On inferring rumor source for SIS model under multiple observations

This paper studies the problem of a single rumor source detection based on the susceptible-infected-susceptible (SIS) spreading model. Based on the rumor centrality proposed in the Susceptible-Infected (SI) model by Shah and Zaman, we propose a rumor centrality based algorithm, that leverages multip...

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Bibliographic Details
Published inInternational Conference on Digital Signal Processing proceedings pp. 755 - 759
Main Authors Wang, Zhaoxu, Zhang, Wenyi, Tan, Chee Wei
Format Conference Proceeding Journal Article
LanguageEnglish
Published IEEE 09.09.2015
Subjects
Algorithms
Approximation algorithms
Computational modeling
Conferences
Detectors
Diffusion
Digital signal processing
Heuristic algorithms
Information dissemination
Joints
maximum likelihood detection
Network topology
Networks
Online social networks
rumor source detection
Silicon
SIS model
statistical inference
Trees
Unions
Online AccessGet full text
ISSN1546-1874
2165-3577
DOI10.1109/ICDSP.2015.7251977

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Summary:This paper studies the problem of a single rumor source detection based on the susceptible-infected-susceptible (SIS) spreading model. Based on the rumor centrality proposed in the Susceptible-Infected (SI) model by Shah and Zaman, we propose a rumor centrality based algorithm, that leverages multiple observations to first construct a diffusion tree graph, and then use the union rumor centrality to find the rumor source. Our simulation results on different network structures shows that our proposed algorithm performs well. For tree networks, increasing the observations can dramatically improve the exact detection probability. This clearly indicates that a richer diversity enhances detect-ability.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Conference-1
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SourceType-Conference Papers & Proceedings-2
ISSN:1546-1874
2165-3577
DOI:10.1109/ICDSP.2015.7251977