Visualizing graphs and clusters as maps

Yifan Hu; Emden R. Gansner; Stephen Kobourov

doi:10.1109/MCG.2010.101

Visualizing graphs and clusters as maps

Yifan Hu, Emden R. Gansner, Stephen Kobourov

Computer Science

Research output: Contribution to journal › Article › peer-review

61 Scopus citations

Abstract

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 language	English (US)
Article number	5567116
Pages (from-to)	54-66
Number of pages	13
Journal	IEEE Computer Graphics and Applications
Volume	30
Issue number	6
DOIs	https://doi.org/10.1109/MCG.2010.101
State	Published - 2010

Keywords

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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10.1109/MCG.2010.101

Cite this

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AB - 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.

KW - clustering

KW - computer graphics

KW - graph coloring

KW - graph drawing

KW - graphics and multimedia

KW - information visualization

KW - maps

KW - set visualization

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Visualizing graphs and clusters as maps

Abstract

Keywords

ASJC Scopus subject areas

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