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

Jianxin Li, Yi Chen, Chengfei Liu, Timos Sellis, Jeffrey Xu Yu, J. Shane Culpepper

Research output: Chapter in Book/Conference paperConference paperpeer-review

10 Citations (Web of Science)


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.

Original languageEnglish
Title of host publicationProceedings - 2017 IEEE 33rd International Conference on Data Engineering, ICDE 2017
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages2
ISBN (Electronic)9781509065431
Publication statusPublished - 16 May 2017
Event33rd IEEE International Conference on Data Engineering, ICDE 2017 - San Diego, United States
Duration: 19 Apr 201722 Apr 2017


Conference33rd IEEE International Conference on Data Engineering, ICDE 2017
Country/TerritoryUnited States
CitySan Diego

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