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The Readability of Path-Preserving Clusterings of Graphs

Daniel Archambault Orcid Logo, Helen C Purchase, Bruno Pinaud

Computer Graphics Forum, Volume: 29, Issue: 3, Pages: 1173 - 1182

Swansea University Author: Daniel Archambault Orcid Logo

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Abstract

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...

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Published in: Computer Graphics Forum
ISSN: 0167-7055
Published: 2010
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URI: https://cronfa.swan.ac.uk/Record/cronfa13912
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spelling 2015-07-01T12:45:36.7145941 v2 13912 2013-01-18 The Readability of Path-Preserving Clusterings of Graphs 8fa6987716a22304ef04d3c3d50ef266 0000-0003-4978-8479 Daniel Archambault Daniel Archambault true false 2013-01-18 SCS 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. Journal Article Computer Graphics Forum 29 3 1173 1182 0167-7055 31 12 2010 2010-12-31 10.1111/j.1467-8659.2009.01683.x COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University 2015-07-01T12:45:36.7145941 2013-01-18T12:33:15.9025958 College of Science Computer Science Daniel Archambault 0000-0003-4978-8479 1 Helen C Purchase 2 Bruno Pinaud 3
title The Readability of Path-Preserving Clusterings of Graphs
spellingShingle The Readability of Path-Preserving Clusterings of Graphs
Daniel Archambault
title_short The Readability of Path-Preserving Clusterings of Graphs
title_full The Readability of Path-Preserving Clusterings of Graphs
title_fullStr The Readability of Path-Preserving Clusterings of Graphs
title_full_unstemmed The Readability of Path-Preserving Clusterings of Graphs
title_sort The Readability of Path-Preserving Clusterings of Graphs
author_id_str_mv 8fa6987716a22304ef04d3c3d50ef266
author_id_fullname_str_mv 8fa6987716a22304ef04d3c3d50ef266_***_Daniel Archambault
author Daniel Archambault
author2 Daniel Archambault
Helen C Purchase
Bruno Pinaud
format Journal article
container_title Computer Graphics Forum
container_volume 29
container_issue 3
container_start_page 1173
publishDate 2010
institution Swansea University
issn 0167-7055
doi_str_mv 10.1111/j.1467-8659.2009.01683.x
college_str College of Science
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hierarchy_top_id collegeofscience
hierarchy_top_title College of Science
hierarchy_parent_id collegeofscience
hierarchy_parent_title College of Science
department_str Computer Science{{{_:::_}}}College of Science{{{_:::_}}}Computer Science
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description 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.
published_date 2010-12-31T03:23:48Z
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