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

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

doi:10.1109/ICDE.2017.15

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

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

School of Physics, Mathematics and Computing

Research output: Chapter in Book/Conference paper › Conference paper › peer-review

11 Citations (Web of Science)

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.

Original language	English
Title of host publication	Proceedings - 2017 IEEE 33rd International Conference on Data Engineering, ICDE 2017
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	17-18
Number of pages	2
ISBN (Electronic)	9781509065431
DOIs	https://doi.org/10.1109/ICDE.2017.15
Publication status	Published - 16 May 2017
Event	33rd IEEE International Conference on Data Engineering, ICDE 2017 - San Diego, United States Duration: 19 Apr 2017 → 22 Apr 2017

Conference

Conference	33rd IEEE International Conference on Data Engineering, ICDE 2017
Country/Territory	United States
City	San Diego
Period	19/04/17 → 22/04/17

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10.1109/ICDE.2017.15

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Li, J., Chen, Y., Liu, C., Sellis, T., Yu, J. X., & Culpepper, J. S. (2017). Personalized influential topic search via social network summarization (Extended abstract). In Proceedings - 2017 IEEE 33rd International Conference on Data Engineering, ICDE 2017 (pp. 17-18). Article 7929911 IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICDE.2017.15

@inproceedings{1c0b9f2d02d144388bb301de7c349a4a,

title = "Personalized influential topic search via social network summarization (Extended abstract)",

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.",

author = "Jianxin Li and Yi Chen and Chengfei Liu and Timos Sellis and Yu, {Jeffrey Xu} and Culpepper, {J. Shane}",

year = "2017",

month = may,

day = "16",

doi = "10.1109/ICDE.2017.15",

language = "English",

pages = "17--18",

booktitle = "Proceedings - 2017 IEEE 33rd International Conference on Data Engineering, ICDE 2017",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

address = "United States",

note = "33rd IEEE International Conference on Data Engineering, ICDE 2017 ; Conference date: 19-04-2017 Through 22-04-2017",

}

Li, J, Chen, Y, Liu, C, Sellis, T, Yu, JX & Culpepper, JS 2017, Personalized influential topic search via social network summarization (Extended abstract). in Proceedings - 2017 IEEE 33rd International Conference on Data Engineering, ICDE 2017., 7929911, IEEE, Institute of Electrical and Electronics Engineers, pp. 17-18, 33rd IEEE International Conference on Data Engineering, ICDE 2017, San Diego, United States, 19/04/17. https://doi.org/10.1109/ICDE.2017.15

Personalized influential topic search via social network summarization (Extended abstract). / Li, Jianxin; Chen, Yi; Liu, Chengfei et al.
Proceedings - 2017 IEEE 33rd International Conference on Data Engineering, ICDE 2017. IEEE, Institute of Electrical and Electronics Engineers, 2017. p. 17-18 7929911.

Research output: Chapter in Book/Conference paper › Conference paper › peer-review

TY - GEN

T1 - Personalized influential topic search via social network summarization (Extended abstract)

AU - Li, Jianxin

AU - Chen, Yi

AU - Liu, Chengfei

AU - Sellis, Timos

AU - Yu, Jeffrey Xu

AU - Culpepper, J. Shane

PY - 2017/5/16

Y1 - 2017/5/16

N2 - 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.

AB - 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.

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DO - 10.1109/ICDE.2017.15

M3 - Conference paper

AN - SCOPUS:85021217877

SP - 17

EP - 18

BT - Proceedings - 2017 IEEE 33rd International Conference on Data Engineering, ICDE 2017

PB - IEEE, Institute of Electrical and Electronics Engineers

T2 - 33rd IEEE International Conference on Data Engineering, ICDE 2017

Y2 - 19 April 2017 through 22 April 2017

ER -

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

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