Source identification under complete observations: A maximum likelihood (ML) source estimator

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

Source identification under complete observations: A maximum likelihood (ML) source estimator

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. 69 - 77
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. 69 - 77	en_US
dc.identifier.uri	http://hdl.handle.net/10453/137314
dc.description.abstract	© Springer Nature Switzerland AG 2019. In this chapter, we introduce a propagation source estimator under complete observations: a maximum likelihood source estimator (Rumor Center). According to Chap. 5, a complete observation presents the exact state for each node in the network at certain time t. This type of observation provides comprehensive knowledge of a transient status of the network. Initial research on propagation source identification focused on complete observations, such as Rumor Center, Dynamic Age, Minimum Description Length, etc. Among these methods, Rumor center is a widely used method. Many variations have been proposed based on this method, such as Local Rumor Center, Multiple Rumor Center, etc. Here, we present the details of the Rumor Center estimator. For the techniques involved in other methods under complete observations, readers could refer to Chap. 9 for details.	en_US
dc.relation.ispartof	Advances in Information Security	en_US
dc.relation.isbasedon	10.1007/978-3-030-02179-5_6	en_US
dc.title	Source identification under complete observations: A maximum likelihood (ML) source estimator	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. In this chapter, we introduce a propagation source estimator under complete observations: a maximum likelihood source estimator (Rumor Center). According to Chap. 5, a complete observation presents the exact state for each node in the network at certain time t. This type of observation provides comprehensive knowledge of a transient status of the network. Initial research on propagation source identification focused on complete observations, such as Rumor Center, Dynamic Age, Minimum Description Length, etc. Among these methods, Rumor center is a widely used method. Many variations have been proposed based on this method, such as Local Rumor Center, Multiple Rumor Center, etc. Here, we present the details of the Rumor Center estimator. For the techniques involved in other methods under complete observations, readers could refer to Chap. 9 for details.

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http://hdl.handle.net/10453/137314