TY - GEN
T1 - The Turing Test for Graph Drawing Algorithms
AU - Purchase, Helen C.
AU - Archambault, Daniel
AU - Kobourov, Stephen
AU - Nöllenburg, Martin
AU - Pupyrev, Sergey
AU - Wu, Hsiang Yun
N1 - Funding Information:
Acknowledgement. We are grateful to all the experimental participants, to Drew Sheets who assisted with creating the graphs in yEd, and to John Hamer who implemented the online experimental system. Ethical approval was given by the University of Arizona Institutional Review Board (ref: 1712113015). This work is supported by NSF grants CCF-1740858, CCF-1712119, DMS-1839274, and FWF grant P 31119.
Funding Information:
We are grateful to all the experimental participants, to Drew Sheets who assisted with creating the graphs in yEd, and to John Hamer who implemented the online experimental system. Ethical approval was given by the University of Arizona Institutional Review Board (ref: 1712113015). This work is supported by NSF grants CCF-1740858, CCF-1712119, DMS-1839274, and FWF grant P 31119.
Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - Do algorithms for drawing graphs pass the Turing Test? That is, are their outputs indistinguishable from graphs drawn by humans? We address this question through a human-centred experiment, focusing on ‘small’ graphs, of a size for which it would be reasonable for someone to choose to draw the graph manually. Overall, we find that hand-drawn layouts can be distinguished from those generated by graph drawing algorithms, although this is not always the case for graphs drawn by force-directed or multi-dimensional scaling algorithms, making these good candidates for Turing Test success. We show that, in general, hand-drawn graphs are judged to be of higher quality than automatically generated ones, although this result varies with graph size and algorithm.
AB - Do algorithms for drawing graphs pass the Turing Test? That is, are their outputs indistinguishable from graphs drawn by humans? We address this question through a human-centred experiment, focusing on ‘small’ graphs, of a size for which it would be reasonable for someone to choose to draw the graph manually. Overall, we find that hand-drawn layouts can be distinguished from those generated by graph drawing algorithms, although this is not always the case for graphs drawn by force-directed or multi-dimensional scaling algorithms, making these good candidates for Turing Test success. We show that, in general, hand-drawn graphs are judged to be of higher quality than automatically generated ones, although this result varies with graph size and algorithm.
KW - Empirical studies
KW - Graph drawing algorithms
KW - Turing test
UR - http://www.scopus.com/inward/record.url?scp=85102732887&partnerID=8YFLogxK
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U2 - 10.1007/978-3-030-68766-3_36
DO - 10.1007/978-3-030-68766-3_36
M3 - Conference contribution
AN - SCOPUS:85102732887
SN - 9783030687656
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 466
EP - 481
BT - Graph Drawing and Network Visualization - 28th International Symposium, GD 2020, Revised Selected Papers
A2 - Auber, David
A2 - Valtr, Pavel
PB - Springer Science and Business Media Deutschland GmbH
T2 - 28th International Symposium on Graph Drawing and Network Visualization, GD 2020
Y2 - 16 September 2020 through 18 September 2020
ER -