DocumentCode :
1818193
Title :
The mental map and memorability in dynamic graphs
Author :
Archambault, Daniel ; Purchase, Helen C.
Author_Institution :
Clique Strategic Res. Cluster, Univ. Coll. Dublin, Dublin, Ireland
fYear :
2012
fDate :
Feb. 28 2012-March 2 2012
Firstpage :
89
Lastpage :
96
Abstract :
In dynamic graph drawing, preserving the mental map, or ensuring that the location of nodes do not change significantly as the information evolves over time is considered an important property by algorithm designers. Many prior experiments have attempted to verify this principle, with surprisingly little success. These experiments have used several different algorithmic methods, a variety of graph interpretation questions on both real and fabricated data, and different presentation methods. However, none of the results have conclusively demonstrated the importance of mental map preservation on task performance. Our experiment measures the efficacy of the dynamic graph drawing in a different manner: we look at how memorable the evolving graph is, rather than how easy it is to interpret. As observed in the previous studies, we found no significant difference in terms of response time or error rate when preserving the mental map. While preserving the mental map is a good idea in principle, we find that it may not always support performance. However, our qualitative data suggests that, in terms of the user´s perception, preserving the mental map makes memorability tasks easier. Our qualitative data also suggests that there may be two features of the dynamic graph drawing that may assist in their memorability: interesting subgraphs that remain visible over time and interesting patterns in node movement. The former is supported by preserving the mental map while the latter is not.
Keywords :
data visualisation; graph theory; information systems; algorithm designers; algorithmic methods; dynamic graph drawing; evolving graph; fabricated data; graph interpretation questions; memorability; mental map preservation; presentation methods; qualitative data; Animation; Context; Educational institutions; Error analysis; Heuristic algorithms; Time factors; Visualization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Visualization Symposium (PacificVis), 2012 IEEE Pacific
Conference_Location :
Songdo
ISSN :
2165-8765
Print_ISBN :
978-1-4673-0863-2
Electronic_ISBN :
2165-8765
Type :
conf
DOI :
10.1109/PacificVis.2012.6183578
Filename :
6183578
Link To Document :
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