Rumor Source Identification in Social Networks with Time-Varying Topology

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

Rumor Source Identification in Social Networks with Time-Varying Topology

Jiang, J Wen, S Yu, S

Xiang, Y Zhou, W

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Dependable and Secure Computing, 2018, 15 (1), pp. 166 - 179
Issue Date:: 2018-01-01

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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	Xiang, Y	en_US
dc.contributor.author	Zhou, W https://orcid.org/0000-0002-1680-2521	en_US
dc.date.issued	2018-01-01	en_US
dc.identifier.citation	IEEE Transactions on Dependable and Secure Computing, 2018, 15 (1), pp. 166 - 179	en_US
dc.identifier.issn	1545-5971	en_US
dc.identifier.uri	http://hdl.handle.net/10453/133131
dc.description.abstract	© 2004-2012 IEEE. Identifying rumor sources in social networks plays a critical role in limiting the damage caused by them through the timely quarantine of the sources. However, the temporal variation in the topology of social networks and the ongoing dynamic processes challenge our traditional source identification techniques that are considered in static networks. In this paper, we borrow an idea from criminology and propose a novel method to overcome the challenges. First, we reduce the time-varying networks to a series of static networks by introducing a time-integrating window. Second, instead of inspecting every individual in traditional techniques, we adopt a reverse dissemination strategy to specify a set of suspects of the real rumor source. This process addresses the scalability issue of source identification problems, and therefore dramatically promotes the efficiency of rumor source identification. Third, to determine the real source from the suspects, we employ a novel microscopic rumor spreading model to calculate the maximum likelihood (ML) for each suspect. The one who can provide the largest ML estimate is considered as the real source. The evaluations are carried out on real social networks with time-varying topology. The experiment results show that our method can reduce 60 - 90 percent of the source seeking area in various time-varying social networks. The results further indicate that our method can accurately identify the real source, or an individual who is very close to the real source. To the best of our knowledge, the proposed method is the first that can be used to identify rumor sources in time-varying social networks.	en_US
dc.relation.ispartof	IEEE Transactions on Dependable and Secure Computing	en_US
dc.relation.isbasedon	10.1109/TDSC.2016.2522436	en_US
dc.subject.classification	Strategic, Defence & Security Studies	en_US
dc.title	Rumor Source Identification in Social Networks with Time-Varying Topology	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	15	en_US
utslib.for	0805 Distributed Computing	en_US
utslib.for	0803 Computer Software	en_US
utslib.for	0804 Data Format	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
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access
pubs.issue	1	en_US
pubs.publication-status	Published	en_US
pubs.volume	15	en_US

Abstract:

© 2004-2012 IEEE. Identifying rumor sources in social networks plays a critical role in limiting the damage caused by them through the timely quarantine of the sources. However, the temporal variation in the topology of social networks and the ongoing dynamic processes challenge our traditional source identification techniques that are considered in static networks. In this paper, we borrow an idea from criminology and propose a novel method to overcome the challenges. First, we reduce the time-varying networks to a series of static networks by introducing a time-integrating window. Second, instead of inspecting every individual in traditional techniques, we adopt a reverse dissemination strategy to specify a set of suspects of the real rumor source. This process addresses the scalability issue of source identification problems, and therefore dramatically promotes the efficiency of rumor source identification. Third, to determine the real source from the suspects, we employ a novel microscopic rumor spreading model to calculate the maximum likelihood (ML) for each suspect. The one who can provide the largest ML estimate is considered as the real source. The evaluations are carried out on real social networks with time-varying topology. The experiment results show that our method can reduce 60 - 90 percent of the source seeking area in various time-varying social networks. The results further indicate that our method can accurately identify the real source, or an individual who is very close to the real source. To the best of our knowledge, the proposed method is the first that can be used to identify rumor sources in time-varying social networks.

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http://hdl.handle.net/10453/133131