Social lens: Searching and browsing communities by content and interaction

Hongyun Cai, Vincent W. Zheng, Penghe Chen, Fanwei Zhu, Kevin Chen Chuan Chang, Zi Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Community analysis is an important task in graph mining. Most of the existing community studies are community detection, which aim to find the community membership for each user based on the user friendship links. However, membership alone, without a complete profile of what a community is and how it interacts with other communities, has limited applications. This motivates us to consider systematically profiling the communities and thereby developing useful community-level applications. In this paper, we introduce a novel concept of community profiling, upon which we build a SocialLens system1 to enable searching and browsing communities by content and interaction. We deploy SocialLens on two social graphs: Twitter and DBLP. We demonstrate two useful applications of SocialLens, including interactive community visualization and profile-Aware community ranking.

Original languageEnglish (US)
Title of host publicationProceedings - 2017 IEEE 33rd International Conference on Data Engineering, ICDE 2017
PublisherIEEE Computer Society
Pages1397-1398
Number of pages2
ISBN (Electronic)9781509065431
DOIs
StatePublished - May 16 2017
Event33rd IEEE International Conference on Data Engineering, ICDE 2017 - San Diego, United States
Duration: Apr 19 2017Apr 22 2017

Publication series

NameProceedings - International Conference on Data Engineering
ISSN (Print)1084-4627

Other

Other33rd IEEE International Conference on Data Engineering, ICDE 2017
CountryUnited States
CitySan Diego
Period4/19/174/22/17

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Information Systems

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  • Cite this

    Cai, H., Zheng, V. W., Chen, P., Zhu, F., Chang, K. C. C., & Huang, Z. (2017). Social lens: Searching and browsing communities by content and interaction. In Proceedings - 2017 IEEE 33rd International Conference on Data Engineering, ICDE 2017 (pp. 1397-1398). [7930090] (Proceedings - International Conference on Data Engineering). IEEE Computer Society. https://doi.org/10.1109/ICDE.2017.194