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Larger crossing angles make graphs easier to read


Huang, W and Eades, P and Hong, S-H, Larger crossing angles make graphs easier to read, Journal of Visual Languages and Computing, 25, (4) pp. 452-465. ISSN 1045-926X (2014) [Refereed Article]

Copyright Statement

Copyright 2014 Elsevier Ltd.

DOI: doi:10.1016/j.jvlc.2014.03.001


Objective: Aesthetics are important in algorithm design and graph evaluation. This paper presents two user studies that were conducted to investigate the impact of crossing angles on human graph comprehension.
   Method and results: These two studies together demonstrate our newly proposed two-step approach for testing graph aesthetics. The first study is a controlled experiment with purposely-generated graphs. Twenty-two subjects participated in the study and were asked to determine the length of a path which was crossed by a set of parallel edges at different angles. The result of an analysis of variance showed that larger crossing angles induced better task performance. The second study was a non-controlled experiment with general real world graphs. Thirty-seven subjects participated in the study and were asked to find the shortest path of two pre-selected nodes in a set of graph drawings. The results of simple regression tests confirmed the negative effect of small crossing angles. This study also showed that among our four proposed candidates, the minimum crossing angle on the path was the best measure for the aesthetic when path finding is important.
   Conclusion: Larger crossing angles make graphs easier to read.
   Implications: In situations where crossings cannot be completely removed (for example, graphs are non-planar, or a drawing convention is applied), or where effort needed to remove all crossings cannot be justified, the crossing angle should be maximized to reduce the negative impact of crossings to the minimum.

Item Details

Item Type:Refereed Article
Keywords:visual perception, information visualization, graph drawing, node-link diagram, aesthetic, crossing angle, edge crossing, perception, evaluation
Research Division:Information and Computing Sciences
Research Group:Library and information studies
Research Field:Human information interaction and retrieval
Objective Division:Expanding Knowledge
Objective Group:Expanding knowledge
Objective Field:Expanding knowledge in the information and computing sciences
UTAS Author:Huang, W (Dr Tony Huang)
ID Code:93323
Year Published:2014
Web of Science® Times Cited:33
Deposited By:Information and Communication Technology
Deposited On:2014-07-26
Last Modified:2018-02-28

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