Personalized influential topic search via social network summarization (Extended abstract)

Li, J; Chen, Y; Liu, C; Sellis, T; Yu, JX; Culpepper, JS

Personalized influential topic search via social network summarization (Extended abstract)

Li, J Chen, Y Liu, C Sellis, T Yu, JX

Culpepper, JS

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Publication Type:: Conference Proceeding
Citation:: Proceedings - International Conference on Data Engineering, 2017, pp. 17 - 18
Issue Date:: 2017-05-16

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Field	Value	Language
dc.contributor.author	Li, J	en_US
dc.contributor.author	Chen, Y	en_US
dc.contributor.author	Liu, C	en_US
dc.contributor.author	Sellis, T	en_US
dc.contributor.author	Yu, JX https://orcid.org/0000-0002-9738-827X	en_US
dc.contributor.author	Culpepper, JS	en_US
dc.date.issued	2017-05-16	en_US
dc.identifier.citation	Proceedings - International Conference on Data Engineering, 2017, pp. 17 - 18	en_US
dc.identifier.isbn	9781509065431	en_US
dc.identifier.issn	1084-4627	en_US
dc.identifier.uri	http://hdl.handle.net/10453/127071
dc.description.abstract	© 2017 IEEE. Social networks have become a vital mechanism to disseminate information to friends and colleagues. But the dynamic nature of information and user connectivity within these networks raised many new and challenging research problems. One of them is the query-related topic search in social networks. In this work, we investigate the important problem of the personalized influential topic search. There are two challenging questions that need to be answered: how to extract the social summarization of the social network so as to measure the topics' influence at the similar granularity scale? and how to apply the social summarization to the problem of personalized influential topic search. Based on the evaluation using real-world datasets, our proposed algorithms are proved to efficient and effective.	en_US
dc.relation.ispartof	Proceedings - International Conference on Data Engineering	en_US
dc.relation.isbasedon	10.1109/ICDE.2017.15	en_US
dc.title	Personalized influential topic search via social network summarization (Extended abstract)	en_US
dc.type	Conference Proceeding
utslib.for	2001 Communication and Media Studies	en_US
utslib.for	0806 Information Systems	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

Abstract:

© 2017 IEEE. Social networks have become a vital mechanism to disseminate information to friends and colleagues. But the dynamic nature of information and user connectivity within these networks raised many new and challenging research problems. One of them is the query-related topic search in social networks. In this work, we investigate the important problem of the personalized influential topic search. There are two challenging questions that need to be answered: how to extract the social summarization of the social network so as to measure the topics' influence at the similar granularity scale? and how to apply the social summarization to the problem of personalized influential topic search. Based on the evaluation using real-world datasets, our proposed algorithms are proved to efficient and effective.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/127071