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Personal and Ubiquitous Computing

, Volume 17, Issue 1, pp 93–105 | Cite as

Establishing aesthetics based on human graph reading behavior: two eye tracking studies

  • Weidong HuangEmail author
Original Article

Abstract

A great deal of real-world data have graph structures, and such structures are often visualized into node-link diagrams for a better understanding of the data. Aesthetic criteria have been used as quality measures to evaluate the effectiveness of graph visualizations in conveying the embedded information to end users. However, commonly applied aesthetics are originally proposed based on common senses and personal intuitions; thus, their relevance to effectiveness is not guaranteed. It has been agreed that aesthetics should be established based on empirical evidence and derived from theories of how people read graphs. As the first step to this end, we have conducted two eye tracking studies in an attempt to understand the underlying mechanism of edge crossings, the most discussed aesthetic, affecting human graph reading performance. These studies lead to the findings of an important aesthetic of crossing angles and a graph reading behavior of geodesic path tendency. We demonstrate that eye tracking is an effective method for gaining insights into how people read graphs and that how aesthetics can be established based on human graph reading behavior.

Keywords

Graph visualization Graph comprehension Aesthetics Edge crossings Crossing angles Geodesic path tendency Eye tracking 

References

  1. 1.
    Angelini P, Cittadini L, Di Battista G, Didimo W, Frati F, Kaufmann M, Symvonis A (2009) On the perspectives opened by right angle crossing drawings. In: Proceedings of the 17th international symposium on graph drawing (GD’09), LNCS, vol 5849, pp 21–32Google Scholar
  2. 2.
    Argyriou EN, Bekos MA, Symvonis A (2010) Maximizing the total resolution of graphs. In: Proceedings of the 18th international symposium on graph drawing (GD’10), LNCS, vol 6502, pp 62–67Google Scholar
  3. 3.
    Carpenter PA, Shah P (1998) A model of the perceptual and conceptual processes in graph comprehension. J Exp Psychol Appl 4:75–100CrossRefGoogle Scholar
  4. 4.
    Convertino G, Chen J, Yost B, Ryu Y, North C (2003) Exploring context switching and cognition in dual-view coordinated visualizations. In: Proceedings of the conference on coordinated and multiple views in exploratory visualization, pp 57–66Google Scholar
  5. 5.
    Di Battista G, Eades P, Tamassia R, Tollis I (1998) Graph drawing: algorithms for the visualization of graphs. Prentice Hall, Englewood CliffsGoogle Scholar
  6. 6.
    Didimo W, Eades P, Liotta G (2009) Drawing graphs with right angle crossings. In: Proceedings of international symposium on algorithms and data structures, pp 206–217Google Scholar
  7. 7.
    Duchowski AT (2003) Eye tracking methodology: theory and practice. Springer, LondonzbMATHGoogle Scholar
  8. 8.
    Dujmovic V, Gudmundsson J, Morin P, Wolle T (2010) Notes on large angle crossing graphs. In: Proceedings of computing: the Australasian theory symposiumGoogle Scholar
  9. 9.
    Duncan J, Humphreys GW (1989) Visual search and stimulus similarity. Psychol Rev 96(3):433–458CrossRefGoogle Scholar
  10. 10.
    Goldberg JH, Kotval XP (1999) Computer interface evaluation using eye movements: methods and constructs. Int J Ind Ergonom 24:631–645CrossRefGoogle Scholar
  11. 11.
    Huang W, Huang M (2010) Exploring the relative importance of crossing number and crossing angle. In: Proceedings of the 2rd international symposium on visual information communication (VINCI’10), Article 10Google Scholar
  12. 12.
    Huang W, Huang M, Lin CC (2011) Aesthetic of angular resolution for node-link diagrams: validation and algorithm. In: Proceedings of 2011 IEEE symposium on visual languages and human-centric computing (VL/HCC’11), Pittsburgh, PA, USA, 18–22 September 2011Google Scholar
  13. 13.
    Huang W, Eades P, Hong S-H (2009) A graph reading behaviour: geodesic path tendency. In: Proceedings of the IEEE Pacific visualization symposium (PacificVis’09). IEEE Press, pp 137–144Google Scholar
  14. 14.
    Huang W, Hong S-H, Eades P (2005) Layout effects on sociogram perception. In: Proceedings of 13th international symposium on graph drawing (GD’05), Lecture Notes in Computer Science. Springer, Berlin, vol 3843, pp 262–273Google Scholar
  15. 15.
    Huang W, Hong S-H, Eades P (2007) Effects of sociogram drawing conventions and edge crossings in social network visualization. J Graph Algorithms Appl (JGAA) 11(2):397–429MathSciNetCrossRefGoogle Scholar
  16. 16.
    Huang W, Hong S-H, Eades P (2008) Effects of crossing angles. In: Proceedings of the IEEE Pacific visualization symposium 2008 (PacificVis’08), pp 41–46Google Scholar
  17. 17.
    Huang W, Eades P (2005) How people read graphs. In: Proceedings of the 2005 Asia-Pacific symposium on information visualisation (APVIS’05), pp 51–58Google Scholar
  18. 18.
    Korner C (2004) Sequential processing in comprehension of hierarchical graphs. Appl Cogn Psychol 18(4):467–480CrossRefGoogle Scholar
  19. 19.
    Korner C (2011) Eye movements reveal distinct search and reasoning processes in comprehension of complex graphs. Appl Cogn Psychol, doi: 10.1002/acp.1766
  20. 20.
    Korner C, Albert D (2002) Speed of comprehension of visualized ordered sets. J Exp Psychol Appl 8:57–71CrossRefGoogle Scholar
  21. 21.
    Krackhardt D (1996) Social networks and liability of newness for managers. Trends Organ Behav 3:159–173Google Scholar
  22. 22.
    Lohse GL (1997) The role of working memory on graphical information processing. Behav Inf Technol 16(6):297–308CrossRefGoogle Scholar
  23. 23.
    McGrath C, Blythe J, Krackhardt D (1997) The effect of spatial arrangement on judgements and errors in interpreting graphs. Soc Netw 19(3):223–242CrossRefGoogle Scholar
  24. 24.
    Nguyen Q, Eades P, Hong S-H, Huang W (2010) Large crossing angles in circular layouts. In: Proceedings of the 18th international symposium on graph drawing (GD’10), 21–24 September, Konstanz, pp 397–399Google Scholar
  25. 25.
    Peebles D, Cheng PCH (2003) Modelling the effect of task and graphical representation on response latency in a graph reading task. Hum Factors 45(1):28–45CrossRefGoogle Scholar
  26. 26.
    Pohl M, Schmitt M, Diehl S (2009) Comparing the readability of graph layouts using eyetracking and task-oriented analysis. In: Proceedings of the fifth international symposium on computational aesthetics in graphics, visualization, and imaging, pp 49–56Google Scholar
  27. 27.
    Purchase H (1997) Which aesthetic has the greatest effect on human understanding? In: Proceedings of the 5th international symposium on graph drawing (GD’97), LNCS. Springer, Berlin, vol 1353, pp 248–261Google Scholar
  28. 28.
    Purchase H, Pilcher C, Plimmer B (2011) Graph drawing aesthetics—created by users not algorithms. IEEE Trans Visual Comput Graph, IEEE Computer SocietyGoogle Scholar
  29. 29.
    Purchase H, Cohen RF, James M (1996) Validating graph drawing aesthetics. In: Proceedings of the symposium on graph drawing (GD’95), LNCS. Springer, Berlin, vol 1027, pp 435–446Google Scholar
  30. 30.
    Ratwani RM, Trafton JG, Boehm-Davis DA (2003) Thinking graphically: extracting local and global information. In Proceedings of the twenty-fifth annual conference of the cognitive science societyGoogle Scholar
  31. 31.
    Ratwani RM, Trafton JG, Boehm-Davis DA (2008) Thinking graphically: connecting vision and cognition during graph comprehension. J Exp Psychol Appl 14(1):36–49CrossRefGoogle Scholar
  32. 32.
    Rayner K (1998) Eye movements in reading and information processing: 20 years of research. Psychol Bull 124(3):372–422CrossRefGoogle Scholar
  33. 33.
    Ware C, Purchase H, Colpoys L, McGill M (2002) Cognitive measurements of graph aesthetics. Inf Visual 1(2):103–110CrossRefGoogle Scholar
  34. 34.
    Wasserman S, Faust K (1994) Social network analysis: methods and applications. Cambridge University Press, CambridgeGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  1. 1.CSIRO ICT CentreEppingAustralia

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