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Record link: 
http://hdl.handle.net/20.500.12708/58252
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Title: 
The Turing Test for Graph Drawing Algorithms
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Citation: 
Purchase, H., Archambault, D., Kobourov, S. G., Nöllenburg, M., Pupyrev, S., & Wu, H.-Y. (2020). The Turing Test for Graph Drawing Algorithms. In D. Archambault (Ed.), Graph Drawing and Network Visualization (GD 2020) (pp. 466–481). IEEE C.P.S. Publishing Services. https://doi.org/10.1007/978-3-030-68766-3_36
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Publisher DOI: 
10.1007/978-3-030-68766-3_36
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Publication Type: 
Inproceedings - Full-Paper Contribution
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Authors: 
Purchase, Helen 
Archambault, Daniel 
Kobourov, Stephen G. 
Nöllenburg, Martin 
Pupyrev, Sergey 
Wu, Hsiang-Yun 
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Organisational Unit: 
E193-02 - Forschungsbereich Computer Graphics 
E193 - Institut für Visual Computing and Human-Centered Technology 
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Published in: 
Graph Drawing and Network Visualization (GD 2020) 
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Volume: 
12590
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Date (published): 
2020
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Number of Pages: 
16
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Publisher: 
IEEE C.P.S. Publishing Services
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Publisher: 
Springer
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Peer reviewed: 
Yes
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Keywords: 
Empirical studies; Graph Drawing Algorithms; Turing Test
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Abstract: 
Do algorithms for drawing graphs pass the Turing Test? Thatis, are their outputs indistinguishable from graphs drawn by humans? Weaddress this question through a human-centred experiment, focusing on`small´ graphs, of a size for which it would be reasonable for someone tochoose to draw the graph manually. Overall, we find that hand-drawnlayouts can be distinguished from those generated by graph drawing al-gorithms, although this is not always the case for graphs drawn by force-directed or multi-dimensional scaling algorithms, making these good can-didates for Turing Test success. We show that, in general, hand-drawngraphs are judged to be of higher quality than automatically generatedones, although this result varies with graph size and algorithm.
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Research Areas: 
Visual Computing and Human-Centered Technology: 100%
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Science Branch: 
Informatik
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Appears in Collections:
Conference Paper

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