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Proceedings Paper

Visualizing node attribute uncertainty in graphs
Author(s): Nathaniel Cesario; Alex Pang; Lisa Singh
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Paper Abstract

Visualizations can potentially misrepresent information if they ignore or hide the uncertainty that are usually present in the data. While various techniques and tools exist for visualizing uncertainty in scientific visualizations, there are very few tools that primarily focus on visualizing uncertainty in graphs or network data. With the popularity of social networks and other data sets that are best represented by graphs, there is a pressing need for visualization systems to show uncertainty that are present in the data. This paper focuses on visualizing a particular type of uncertainty in graphs - we assume that nodes in a graph can have one or more attributes, and each of these attributes may have an uncertainty associated with it. Unlike previous efforts in visualizing node or edge uncertainty in graphs by changing the appearance of the nodes or edges, e.g. by blurring, the approach in this paper is to use the spatial layout of the graph to represent the uncertainty information. We describe a prototype tool that incorporates several uncertainty-to-spatial-layout mappings and describe a scenario showing how it might be used for a visual analysis task.

Paper Details

Date Published: 24 January 2011
PDF: 13 pages
Proc. SPIE 7868, Visualization and Data Analysis 2011, 78680H (24 January 2011); doi: 10.1117/12.872677
Show Author Affiliations
Nathaniel Cesario, Univ. of California, Santa Cruz (United States)
Alex Pang, Univ. of California, Santa Cruz (United States)
Lisa Singh, Georgetown Univ. (United States)


Published in SPIE Proceedings Vol. 7868:
Visualization and Data Analysis 2011
Pak Chung Wong; Jinah Park; Ming C. Hao; Chaomei Chen; Katy Börner; David L. Kao; Jonathan C. Roberts, Editor(s)

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