iTopic: Influential Topic Discovery from Information Networks via Keyword Query

Jianxin Li, Chengfei Liu, Lu Chen, Zhenying He, Amitava Datta, Feng Xia

Research output: Chapter in Book/Conference paperConference paper

3 Citations (Scopus)

Abstract

The rapid growth of information networks provides a significant
opportunity for people to learn the world and find useful information
for decision making. To find influential topics in a given
context, instead of searching widely over the whole information
network, normally it is wise to find the related communities first
and then identify the influential topics in those communities. In
this demonstration, we present a novel framework to compute the
correlated sub-networks from a large information network such as
CiteSeerX based on a user’s keyword query, and to extract the influential
topics from each correlated network. To help users understand
the influential topics as a whole, we utilize a word cloud
to represent the discovered topics for each correlated network. As
such, multiple word clouds can be generated for different correlated
networks, by which users can easily pick up their interested
ones by reading the visualized topic descriptions over word clouds.
To determine the sizes of different terms in a word cloud, we introduce
a scoring scheme for assessing the influence of these terms
in the corresponding networks. We demonstrate the functionality
of our influential topic system, called iTopic, using the CiteSeerX
information network data.
Original languageEnglish
Title of host publicationProceedings of the 26th International Conference on World Wide Web
Place of PublicationSwitzerland
PublisherInternational World Wide Web Conference Committee
Pages231-235
Number of pages5
ISBN (Electronic)9781450349147
DOIs
Publication statusPublished - 2017
Event26th International World Wide Web Conference, WWW 2017 Companion - Perth, Australia
Duration: 3 Apr 20177 Apr 2017

Conference

Conference26th International World Wide Web Conference, WWW 2017 Companion
CountryAustralia
CityPerth
Period3/04/177/04/17

Fingerprint

Demonstrations
Decision making

Cite this

Li, J., Liu, C., Chen, L., He, Z., Datta, A., & Xia, F. (2017). iTopic: Influential Topic Discovery from Information Networks via Keyword Query. In Proceedings of the 26th International Conference on World Wide Web (pp. 231-235). Switzerland: International World Wide Web Conference Committee. https://doi.org/10.1145/3041021.3054719
Li, Jianxin ; Liu, Chengfei ; Chen, Lu ; He, Zhenying ; Datta, Amitava ; Xia, Feng. / iTopic: Influential Topic Discovery from Information Networks via Keyword Query. Proceedings of the 26th International Conference on World Wide Web. Switzerland : International World Wide Web Conference Committee, 2017. pp. 231-235
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abstract = "The rapid growth of information networks provides a significantopportunity for people to learn the world and find useful informationfor decision making. To find influential topics in a givencontext, instead of searching widely over the whole informationnetwork, normally it is wise to find the related communities firstand then identify the influential topics in those communities. Inthis demonstration, we present a novel framework to compute thecorrelated sub-networks from a large information network such asCiteSeerX based on a user’s keyword query, and to extract the influentialtopics from each correlated network. To help users understandthe influential topics as a whole, we utilize a word cloudto represent the discovered topics for each correlated network. Assuch, multiple word clouds can be generated for different correlatednetworks, by which users can easily pick up their interestedones by reading the visualized topic descriptions over word clouds.To determine the sizes of different terms in a word cloud, we introducea scoring scheme for assessing the influence of these termsin the corresponding networks. We demonstrate the functionalityof our influential topic system, called iTopic, using the CiteSeerXinformation network data.",
author = "Jianxin Li and Chengfei Liu and Lu Chen and Zhenying He and Amitava Datta and Feng Xia",
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Li, J, Liu, C, Chen, L, He, Z, Datta, A & Xia, F 2017, iTopic: Influential Topic Discovery from Information Networks via Keyword Query. in Proceedings of the 26th International Conference on World Wide Web. International World Wide Web Conference Committee, Switzerland, pp. 231-235, 26th International World Wide Web Conference, WWW 2017 Companion, Perth, Australia, 3/04/17. https://doi.org/10.1145/3041021.3054719

iTopic: Influential Topic Discovery from Information Networks via Keyword Query. / Li, Jianxin; Liu, Chengfei; Chen, Lu; He, Zhenying; Datta, Amitava; Xia, Feng.

Proceedings of the 26th International Conference on World Wide Web. Switzerland : International World Wide Web Conference Committee, 2017. p. 231-235.

Research output: Chapter in Book/Conference paperConference paper

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Li J, Liu C, Chen L, He Z, Datta A, Xia F. iTopic: Influential Topic Discovery from Information Networks via Keyword Query. In Proceedings of the 26th International Conference on World Wide Web. Switzerland: International World Wide Web Conference Committee. 2017. p. 231-235 https://doi.org/10.1145/3041021.3054719