On Inferring Rumor Source for SIS Model under Multiple Observations

Zhaoxu Wang; Wenyi Zhang; Chee Wei Tan

doi:10.1109/ICDSP.2015.7251977

On Inferring Rumor Source for SIS Model under Multiple Observations

Zhaoxu Wang
, Wenyi Zhang
, Chee Wei Tan

Department of Computer Science

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

13 Citations (Scopus)

Abstract

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.

Original language	English
Title of host publication	DSP 2015 - 2015 IEEE International Conference on Digital Signal Processing (DSP)
Publisher	IEEE
Pages	755-759
ISBN (Print)	9781479980581, 9781479980574, 9781479980598
DOIs	https://doi.org/10.1109/ICDSP.2015.7251977
Publication status	Published - Jul 2015
Event	2015 IEEE International Conference on Digital Signal Processing (DSP 2015) - , Singapore Duration: 21 Jul 2015 → 24 Jul 2015

Publication series

Name
ISSN (Print)	1546-1874
ISSN (Electronic)	2165-3577

Conference

Conference	2015 IEEE International Conference on Digital Signal Processing (DSP 2015)
Place	Singapore
Period	21/07/15 → 24/07/15

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Research Keywords

maximum likelihood detection
Online social networks
rumor source detection
SIS model
statistical inference

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10.1109/ICDSP.2015.7251977

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Cite this

@inproceedings{69daff82f29a406584cd993f5e775c20,

title = "On Inferring Rumor Source for SIS Model under Multiple Observations",

abstract = "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.",

keywords = "maximum likelihood detection, Online social networks, rumor source detection, SIS model, statistical inference",

author = "Zhaoxu Wang and Wenyi Zhang and Tan, \{Chee Wei\}",

year = "2015",

month = jul,

doi = "10.1109/ICDSP.2015.7251977",

language = "English",

isbn = "9781479980581",

publisher = "IEEE",

pages = "755--759",

booktitle = "DSP 2015 - 2015 IEEE International Conference on Digital Signal Processing (DSP)",

address = "United States",

note = "2015 IEEE International Conference on Digital Signal Processing (DSP 2015) ; Conference date: 21-07-2015 Through 24-07-2015",

}

On Inferring Rumor Source for SIS Model under Multiple Observations. / Wang, Zhaoxu; Zhang, Wenyi; Tan, Chee Wei.
DSP 2015 - 2015 IEEE International Conference on Digital Signal Processing (DSP). IEEE, 2015. p. 755-759 7251977.

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - On Inferring Rumor Source for SIS Model under Multiple Observations

AU - Wang, Zhaoxu

AU - Zhang, Wenyi

AU - Tan, Chee Wei

PY - 2015/7

Y1 - 2015/7

N2 - 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.

AB - 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.

KW - maximum likelihood detection

KW - Online social networks

KW - rumor source detection

KW - SIS model

KW - statistical inference

UR - https://www.scopus.com/pages/publications/84961353079

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-84961353079&origin=recordpage

U2 - 10.1109/ICDSP.2015.7251977

DO - 10.1109/ICDSP.2015.7251977

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9781479980581

SN - 9781479980574

SN - 9781479980598

SP - 755

EP - 759

BT - DSP 2015 - 2015 IEEE International Conference on Digital Signal Processing (DSP)

PB - IEEE

T2 - 2015 IEEE International Conference on Digital Signal Processing (DSP 2015)

Y2 - 21 July 2015 through 24 July 2015

ER -

On Inferring Rumor Source for SIS Model under Multiple Observations

Abstract

Publication series

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UN SDGs

Research Keywords

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