Mining top K spread sources for a specific topic and a given node

Liu, W; Deng, ZH; Cao, L; Xu, X; Liu, H; Gong, X

Mining top K spread sources for a specific topic and a given node

Liu, W Deng, ZH Cao, L

Xu, X Liu, H Gong, X

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Cybernetics, 2015, 45 (11), pp. 2472 - 2483
Issue Date:: 2015-11-01

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Field	Value	Language
dc.contributor.author	Liu, W	en_US
dc.contributor.author	Deng, ZH	en_US
dc.contributor.author	Cao, L https://orcid.org/0000-0003-1562-9429	en_US
dc.contributor.author	Xu, X	en_US
dc.contributor.author	Liu, H	en_US
dc.contributor.author	Gong, X	en_US
dc.date.issued	2015-11-01	en_US
dc.identifier.citation	IEEE Transactions on Cybernetics, 2015, 45 (11), pp. 2472 - 2483	en_US
dc.identifier.issn	2168-2267	en_US
dc.identifier.uri	http://hdl.handle.net/10453/121759
dc.description.abstract	© 2013 IEEE. In social networks, nodes (or users) interested in specific topics are often influenced by others. The influence is usually associated with a set of nodes rather than a single one. An interesting but challenging task for any given topic and node is to find the set of nodes that represents the source or trigger for the topic and thus identify those nodes that have the greatest influence on the given node as the topic spreads. We find that it is an NP-hard problem. This paper proposes an effective framework to deal with this problem. First, the topic propagation is represented as the Bayesian network. We then construct the propagation model by a variant of the voter model. The probability transition matrix (PTM) algorithm is presented to conduct the probability inference with the complexity O{θ3log2θ), while θ is the number nodes in the given graph. To evaluate the PTM algorithm, we conduct extensive experiments on real datasets. The experimental results show that the PTM algorithm is both effective and efficient.	en_US
dc.relation.ispartof	IEEE Transactions on Cybernetics	en_US
dc.relation.isbasedon	10.1109/TCYB.2014.2375185	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Mining top K spread sources for a specific topic and a given node	en_US
dc.type	Journal Article
utslib.citation.volume	11	en_US
utslib.citation.volume	45	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0102 Applied Mathematics	en_US
utslib.for	0906 Electrical and Electronic Engineering	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/Strength - AAI - Advanced Analytics Institute Research Centre
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	closed_access
pubs.issue	11	en_US
pubs.publication-status	Published	en_US
pubs.volume	45	en_US

Abstract:

© 2013 IEEE. In social networks, nodes (or users) interested in specific topics are often influenced by others. The influence is usually associated with a set of nodes rather than a single one. An interesting but challenging task for any given topic and node is to find the set of nodes that represents the source or trigger for the topic and thus identify those nodes that have the greatest influence on the given node as the topic spreads. We find that it is an NP-hard problem. This paper proposes an effective framework to deal with this problem. First, the topic propagation is represented as the Bayesian network. We then construct the propagation model by a variant of the voter model. The probability transition matrix (PTM) algorithm is presented to conduct the probability inference with the complexity O{θ3log2θ), while θ is the number nodes in the given graph. To evaluate the PTM algorithm, we conduct extensive experiments on real datasets. The experimental results show that the PTM algorithm is both effective and efficient.

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http://hdl.handle.net/10453/121759