No Cover Image

Journal article 614 views

The Readability of Path-Preserving Clusterings of Graphs / Daniel Archambault; Helen C Purchase; Bruno Pinaud

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

Swansea University Author: Daniel, Archambault

Full text not available from this repository: check for access using links below.

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...

Full description

Published in: Computer Graphics Forum
ISSN: 0167-7055
Published: 2010
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa13912
Tags: Add Tag
No Tags, Be the first to tag this record!
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 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.
College: College of Science
Issue: 3
Start Page: 1173
End Page: 1182