HiMap: Adaptive visualization of large-scale online social networks

Lei Shi, Nan Cao, Shixia Liu, Weihong Qian, Li Tan, Guodong Wang, Jimeng Sun, Ching Yung Lin

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

Abstract

Visualizing large-scale online social network is a challenging yet essential task. This paper presents HiMap, a system that visualizes it by clustered graph via hierarchical grouping and summarization. HiMap employs a novel adaptive data loading technique to accurately control the visual density of each graph view, and along with the optimized layout algorithm and the two kinds of edge bundling methods, to effectively avoid the visual clutter commonly found in previous social network visualization tools. HiMap also provides an integrated suite of interactions to allow the users to easily navigate the social map with smooth and coherent view transitions to keep their momentum. Finally, we confirm the effectiveness of HiMap algorithms through graph-travesal based evaluations.

Original languageEnglish (US)
Title of host publicationIEEE Pacific Visualization Symposium, PacificVis 2009 - Proceedings
Pages41-48
Number of pages8
DOIs
StatePublished - Jul 21 2009
Externally publishedYes
EventIEEE Pacific Visualization Symposium, PacificVis 2009 - Beijing, China
Duration: Apr 20 2009Apr 23 2009

Publication series

NameIEEE Pacific Visualization Symposium, PacificVis 2009 - Proceedings

Conference

ConferenceIEEE Pacific Visualization Symposium, PacificVis 2009
CountryChina
CityBeijing
Period4/20/094/23/09

Keywords

  • Adaptive visualization
  • Clustered graph
  • H.5 [user interfaces] graphical user interfaces
  • I.3 [methodology and techniques] interaction techniques
  • Social network visualization

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition
  • Software

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