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dc.contributor.authorYu, Pei-Duoen_US
dc.contributor.authorTan, Chee Weien_US
dc.contributor.authorFu, Hung-Linen_US
dc.date.accessioned2018-08-21T05:57:05Z-
dc.date.available2018-08-21T05:57:05Z-
dc.date.issued2017-01-01en_US
dc.identifier.issn2475-420Xen_US
dc.identifier.urihttp://hdl.handle.net/11536/147038-
dc.description.abstractDetecting information source in viral spreading has important applications such as to root out the culprit of a rumor spreading in online social networks. In particular, given a snapshot observation of the network topology of nodes having the rumor, how to accurately identify the initial source of the spreading? In the seminal work [Shah et el. 2011], this problem was formulated as a maximum likelihood estimation problem and solved using a rumor centrality approach for graphs that are degree-regular trees. The case of graphs with cycles is an open problem. In this paper, we address the maximum likelihood estimation problem by a generalized rumor centrality for spreading in unicyclic graphs. In particular, we derive a generalized rumor centrality that leads to a new graph-theoretic design approach to inference algorithms.en_US
dc.language.isoen_USen_US
dc.titleRumor Source Detection in Unicyclic Graphsen_US
dc.typeProceedings Paperen_US
dc.identifier.journal2017 IEEE INFORMATION THEORY WORKSHOP (ITW)en_US
dc.citation.spage439en_US
dc.citation.epage443en_US
dc.contributor.department交大名義發表zh_TW
dc.contributor.departmentNational Chiao Tung Universityen_US
dc.identifier.wosnumberWOS:000426901500089en_US
Appears in Collections:Conferences Paper