On the Visualization of Social and other Scale-Free Networks
Yuntao Jia
Hoberock, J.
Garland, M.
Hart, J.
Illinois Univ., Urbana, IL;
This paper appears in: Visualization and Computer Graphics, IEEE Transactions on
Publication Date: Nov.-Dec. 2008
Volume: 14,
Issue: 6
On page(s): 1285-1292
ISSN: 1077-2626
INSPEC Accession Number: 10301191
Digital Object Identifier: 10.1109/TVCG.2008.151
Current Version Published: 2008-10-24
Abstract
This paper proposes novel methods for visualizing specifically the large power-law graphs that arise in sociology and the sciences. In such cases a large portion of edges can be shown to be less important and removed while preserving component connectedness and other features (e.g. cliques) to more clearly reveal the networkpsilas underlying connection pathways. This simplification approach deterministically filters (instead of clustering) the graph to retain important node and edge semantics, and works both automatically and interactively. The improved graph filtering and layout is combined with a novel computer graphics anisotropic shading of the dense crisscrossing array of edges to yield a full social network and scale-free graph visualization system. Both quantitative analysis and visual results demonstrate the effectiveness of this approach.
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