GMap: Visualizing graphs and clusters as maps

Emden R. Gansner, Yifan Hu, Stephen Kobourov

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

66 Scopus citations

Abstract

Information visualization is essential in making sense out of large data sets. Often, high-dimensional data are visualized as a collection of points in 2-dimensional space through dimensionality reduction techniques. However, these traditional methods often do not capture well the underlying structural information, clustering, and neighborhoods. In this paper, we describe GMap, a practical algorithm for visualizing relational data with geographic-like maps. We illustrate the effectiveness of this approach with examples from several domains.

Original languageEnglish (US)
Title of host publicationIEEE Pacific Visualization Symposium 2010, PacificVis 2010 - Proceedings
Pages201-208
Number of pages8
DOIs
StatePublished - May 6 2010
EventIEEE Pacific Visualization Symposium 2010, PacificVis 2010 - Taipei, Taiwan, Province of China
Duration: Mar 2 2010Mar 5 2010

Publication series

NameIEEE Pacific Visualization Symposium 2010, PacificVis 2010 - Proceedings

Other

OtherIEEE Pacific Visualization Symposium 2010, PacificVis 2010
CountryTaiwan, Province of China
CityTaipei
Period3/2/103/5/10

Keywords

  • G.2 [discrete mathematics]: Graph theory - [H.3]: Information storage and retrieval - Clustering

ASJC Scopus subject areas

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

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  • Cite this

    Gansner, E. R., Hu, Y., & Kobourov, S. (2010). GMap: Visualizing graphs and clusters as maps. In IEEE Pacific Visualization Symposium 2010, PacificVis 2010 - Proceedings (pp. 201-208). [5429590] (IEEE Pacific Visualization Symposium 2010, PacificVis 2010 - Proceedings). https://doi.org/10.1109/PACIFICVIS.2010.5429590