Identifying propagation source in large-scale networks

Jiang, J; Wen, S; Yu, S; Liu, B; Xiang, Y; Zhou, W

Identifying propagation source in large-scale networks

Jiang, J Wen, S Yu, S

Liu, B

Xiang, Y Zhou, W

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Publication Type:: Chapter
Citation:: Advances in Information Security, 2019, 73 pp. 159 - 178
Issue Date:: 2019-01-01

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Field	Value	Language
dc.contributor.author	Jiang, J	en_US
dc.contributor.author	Wen, S	en_US
dc.contributor.author	Yu, S https://orcid.org/0000-0003-4485-6743	en_US
dc.contributor.author	Liu, B https://orcid.org/0000-0002-3603-6617	en_US
dc.contributor.author	Xiang, Y	en_US
dc.contributor.author	Zhou, W https://orcid.org/0000-0002-1680-2521	en_US
dc.date.issued	2019-01-01	en_US
dc.identifier.citation	Advances in Information Security, 2019, 73 pp. 159 - 178	en_US
dc.identifier.uri	http://hdl.handle.net/10453/137330
dc.description.abstract	© Springer Nature Switzerland AG 2019. The global diffusion of epidemics, rumors and computer viruses causes great damage to our society. It is critical to identify the diffusion sources and promptly quarantine them. However, one critical issue of current methods is that they are far are unsuitable for large-scale networks due to the computational cost and the complex spatiotemporal diffusion processes. In this chapter, we introduce a community structure based approach to efficiently identify diffusion sources in large networks.	en_US
dc.relation.ispartof	Advances in Information Security	en_US
dc.relation.isbasedon	10.1007/978-3-030-02179-5_12	en_US
dc.title	Identifying propagation source in large-scale networks	en_US
dc.type	Chapter
utslib.citation.volume	73	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	73	en_US

Abstract:

© Springer Nature Switzerland AG 2019. The global diffusion of epidemics, rumors and computer viruses causes great damage to our society. It is critical to identify the diffusion sources and promptly quarantine them. However, one critical issue of current methods is that they are far are unsuitable for large-scale networks due to the computational cost and the complex spatiotemporal diffusion processes. In this chapter, we introduce a community structure based approach to efficiently identify diffusion sources in large networks.

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http://hdl.handle.net/10453/137330