Exploring the limits of complexity: a survey of empirical studies on graph visualisation

Vahan Yoghourdjian, Daniel Archambault, Stephan Diehl, Tim Dwyer, Karsten Klein, Helen C. Purchase, Hsiang Yun Wu

Research output: Contribution to journalArticleResearchpeer-review

13 Citations (Scopus)

Abstract

For decades, researchers in information visualisation and graph drawing have focused on developing techniques for the layout and display of very large and complex networks. Experiments involving human participants have also explored the readability of different styles of layout and representations for such networks. In both bodies of literature, networks are frequently referred to as being ‘large’ or ‘complex’, yet these terms are relative. From a human-centred, experiment point-of-view, what constitutes ‘large’ (for example) depends on several factors, such as data complexity, visual complexity, and the technology used. In this paper, we survey the literature on human-centred experiments to understand how, in practice, different features and characteristics of node–link diagrams affect visual complexity.

Original languageEnglish
Pages (from-to)264-282
Number of pages19
JournalVisual Informatics
Volume2
Issue number4
DOIs
Publication statusPublished - Dec 2018

Keywords

  • Cognitive scalability
  • Empirical studies
  • Evaluations
  • Graph visualisation
  • Network visualisation
  • node–link diagrams

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