The Turing Test for Graph Drawing Algorithms

Helen C. Purchase, Daniel Archambault, Stephen Kobourov, Martin Nöllenburg, Sergey Pupyrev, Hsiang Yun Wu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish (US)
Title of host publicationGraph Drawing and Network Visualization - 28th International Symposium, GD 2020, Revised Selected Papers
EditorsDavid Auber, Pavel Valtr
PublisherSpringer Science and Business Media Deutschland GmbH
Pages466-481
Number of pages16
ISBN (Print)9783030687656
DOIs
StatePublished - 2020
Event28th International Symposium on Graph Drawing and Network Visualization, GD 2020 - Virtual, Online
Duration: Sep 16 2020Sep 18 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12590 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference28th International Symposium on Graph Drawing and Network Visualization, GD 2020
CityVirtual, Online
Period9/16/209/18/20

Keywords

  • Empirical studies
  • Graph drawing algorithms
  • Turing test

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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