Journal article 1673 views 484 downloads
Can animation support the visualisation of dynamic graphs?
Information Sciences, Volume: 330, Pages: 495 - 509
Swansea University Author: Daniel Archambault
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DOI (Published version): 10.1016/j.ins.2015.04.017
Abstract
Animation and small multiples are methods for visualizing dynamically evolving graphs. Animations present an interactive movie of the data where positions of nodes are smoothly interpolated as the graph evolves. Nodes fade in/out as they are added/removed from the data set. Small multiples presents...
Published in: | Information Sciences |
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ISSN: | 00200255 |
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2016
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URI: | https://cronfa.swan.ac.uk/Record/cronfa20860 |
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2019-07-17T15:11:36.1651561 v2 20860 2015-04-24 Can animation support the visualisation of dynamic graphs? 8fa6987716a22304ef04d3c3d50ef266 0000-0003-4978-8479 Daniel Archambault Daniel Archambault true false 2015-04-24 MACS Animation and small multiples are methods for visualizing dynamically evolving graphs. Animations present an interactive movie of the data where positions of nodes are smoothly interpolated as the graph evolves. Nodes fade in/out as they are added/removed from the data set. Small multiples presents the data like a comic book with the graph at various states in separate windows. The user scans these windows to see how the data evolves. In a recent experiment, drawing stability (known more widely as the “mental map”) was shown to help users follow specific nodes or long paths in dynamically evolving data. However, no significant difference between animation and small multiples presentations was found. In this paper, we look at data where the nodes in the graph have low drawing stability and analyze it with new error metrics: measuring how close the given answer is from the correct answer on a continuous scale. We find evidence that when the stability of the drawing is low and important nodes in the task cannot be highlighted throughout the time series, animation can improve task performance when compared to the use of small multiples. Journal Article Information Sciences 330 495 509 00200255 10 2 2016 2016-02-10 10.1016/j.ins.2015.04.017 COLLEGE NANME Mathematics and Computer Science School COLLEGE CODE MACS Swansea University 2019-07-17T15:11:36.1651561 2015-04-24T11:50:00.8334992 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Daniel Archambault 0000-0003-4978-8479 1 Helen C. Purchase 2 0020860-28042015144237.pdf maplikeJournalSIR1.pdf 2015-04-28T14:42:37.5430000 Output 65349899 application/pdf Author's Original true 2016-04-17T00:00:00.0000000 true |
title |
Can animation support the visualisation of dynamic graphs? |
spellingShingle |
Can animation support the visualisation of dynamic graphs? Daniel Archambault |
title_short |
Can animation support the visualisation of dynamic graphs? |
title_full |
Can animation support the visualisation of dynamic graphs? |
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Can animation support the visualisation of dynamic graphs? |
title_full_unstemmed |
Can animation support the visualisation of dynamic graphs? |
title_sort |
Can animation support the visualisation of dynamic graphs? |
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8fa6987716a22304ef04d3c3d50ef266 |
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Daniel Archambault |
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Daniel Archambault Helen C. Purchase |
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Information Sciences |
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Animation and small multiples are methods for visualizing dynamically evolving graphs. Animations present an interactive movie of the data where positions of nodes are smoothly interpolated as the graph evolves. Nodes fade in/out as they are added/removed from the data set. Small multiples presents the data like a comic book with the graph at various states in separate windows. The user scans these windows to see how the data evolves. In a recent experiment, drawing stability (known more widely as the “mental map”) was shown to help users follow specific nodes or long paths in dynamically evolving data. However, no significant difference between animation and small multiples presentations was found. In this paper, we look at data where the nodes in the graph have low drawing stability and analyze it with new error metrics: measuring how close the given answer is from the correct answer on a continuous scale. We find evidence that when the stability of the drawing is low and important nodes in the task cannot be highlighted throughout the time series, animation can improve task performance when compared to the use of small multiples. |
published_date |
2016-02-10T12:42:08Z |
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11.048042 |