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

Document Type

Conference Proceeding

Publication Date

5-16-2017

Abstract

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.

Identifier

85021217877 (Scopus)

ISBN

[9781509065431]

Publication Title

Proceedings International Conference on Data Engineering

External Full Text Location

https://doi.org/10.1109/ICDE.2017.15

ISSN

10844627

First Page

17

Last Page

18

Grant

DE140100275

Fund Ref

Australian Research Council

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