Visualizing graphs and clusters as maps

Yifan Hu, Emden R. Gansner, Stephen Kobourov

Research output: Contribution to journalArticlepeer-review

60 Scopus citations


Information visualization is essential in making sense of large datasets. Often, high-dimensional data are visualized as a collection of points in 2D space through dimensionality reduction techniques. However, these traditional methods often don't capture the underlying structural information, clustering, and neighborhoods well. GMap is a practical algorithmic framework for visualizing relational data with geographic-like maps. This approach is effective in various domains.

Original languageEnglish (US)
Article number5567116
Pages (from-to)54-66
Number of pages13
JournalIEEE Computer Graphics and Applications
Issue number6
StatePublished - 2010


  • clustering
  • computer graphics
  • graph coloring
  • graph drawing
  • graphics and multimedia
  • information visualization
  • maps
  • set visualization

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design


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