The Readability of Path-Preserving Clusterings of Graphs

Graph visualization systems often exploit opaque metanodes to reduce visual clutter and improve the readability of large graphs. This filtering can be done in a path‐preserving way based on attribute values associated with the nodes of the graph. Despite extensive use of these representations, as fa...

Full description

Saved in:
Bibliographic Details
Published in:Computer graphics forum Vol. 29; no. 3; pp. 1173 - 1182
Main Authors: Archambault, Daniel, Purchase, Helen C., Pinaud, Bruno
Format: Journal Article
Language:English
Published: Oxford, UK Blackwell Publishing Ltd 01.06.2010
Wiley
Subjects:
C (programming language)
Clusters
Clutter
Computer graphics
Computer Science
Filtering
Filtration
G.2.2 [Discrete Mathematics]: Graph Theory-Graph Algorithms
Graphs
H.1.2 [Information Systems]: User/Machine Systems-Human Factors
Other
Readability
Representations
Studies
Visual
ISSN:0167-7055, 1467-8659
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Graph visualization systems often exploit opaque metanodes to reduce visual clutter and improve the readability of large graphs. This filtering can be done in a path‐preserving way based on attribute values associated with the nodes of the graph. Despite extensive use of these representations, as far as we know, no formal experimentation exists to evaluate if they improve the readability of graphs. In this paper, we present the results of a user study that formally evaluates how such representations affect the readability of graphs. We also explore the effect of graph size and connectivity in terms of this primary research question. Overall, for our tasks, we did not find a significant difference when this clustering is used. However, if the graph is highly connected, these clusterings can improve performance. Also, if the graph is large enough and can be simplified into a few metanodes, benefits in performance on global tasks are realized. Under these same conditions, however, performance of local attribute tasks may be reduced.
Bibliography:ark:/67375/WNG-7HHVKXC7-6
istex:D7A0D93846F1F339CB8ACDAA3A300629EF3CD768
ArticleID:CGF1683
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ObjectType-Article-2
content type line 23
ISSN:0167-7055
1467-8659
DOI:10.1111/j.1467-8659.2009.01683.x