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Overall quality evaluation of graph layouts based on regression analysis

Published: 14 August 2017 Publication History

Abstract

Combining1 subjective evaluation with aesthetic criteria, this paper proposes an objective overall quality assessment method for graph layout algorithms. Firstly, we build the subjective rating database of graph layouts. The subjective experiment is designed to rate different graph layouts. Then, for each graph layout, we use the readability metrics of the layout as independent variables, the subjective score of users as the dependent variable, to establish the regression model. Through the regression model, we can get the overall quality score of a graph layout.

References

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Tamassia R. On embedding a graph in the grid with the minimum number of bends{J}. 1987, 16(3):421--444.
[2]
Biedl T C, Marks J, Ryall K, et al. Graph Multidrawing: Finding Nice Drawings Without Defining Nice{C}// International Symposium on Graph Drawing. Springer-Verlag, 1998:347--355.
[3]
Purchase H. Which aesthetic has the greatest effect on human understanding?{C}//International Symposium on Graph Drawing. Springer Berlin Heidelberg, 1997: 248--261.
[4]
Ware C, Purchase H, Colpoys L, et al. Cognitive measurements of graph aesthetics{J}. Information Visualization, 2002, 1(2): 103--110.
[5]
Huang W, Huang M L, Lin C C. Evaluating overall quality of graph visualizations based on aesthetics aggregation{J}. Information Sciences, 2016, 330: 444--454.
[6]
Purchase H. Which aesthetic has the greatest effect on human understanding?{C}//International Symposium on Graph Drawing. Springer Berlin Heidelberg, 1997: 248--261

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  1. Overall quality evaluation of graph layouts based on regression analysis

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      cover image ACM Other conferences
      VINCI '17: Proceedings of the 10th International Symposium on Visual Information Communication and Interaction
      August 2017
      158 pages
      ISBN:9781450352925
      DOI:10.1145/3105971
      Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

      Sponsors

      • KMUTT: King Mongkut's University of Technology Thonburi

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 14 August 2017

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      Author Tags

      1. evaluation model
      2. graph layout
      3. overall quality
      4. regression analysis

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      • Poster

      Funding Sources

      • Natural Science Foundation of China
      • Specialized Research Fund for the Doctoral Program of Higher Education

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      VINCI '17
      Sponsor:
      • KMUTT

      Acceptance Rates

      VINCI '17 Paper Acceptance Rate 12 of 27 submissions, 44%;
      Overall Acceptance Rate 71 of 193 submissions, 37%

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