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Euclidean, Hyperbolic, and Spherical Networks: An Empirical Study of Matching Network Structure to Best Visualizations.

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Title: Euclidean, Hyperbolic, and Spherical Networks: An Empirical Study of Matching Network Structure to Best Visualizations.
Authors: Miller, Jacob1 (AUTHOR), Bhatia, Dhruv2 (AUTHOR), Purchase, Helen3 (AUTHOR), Kobourov, Stephen1,2 (AUTHOR)
Source: Computer Graphics Forum. Jun2025, Vol. 44 Issue 3, p1-12. 12p.
Subjects: Euclidean geometry, Spherical geometry, Task performance, Cognitive processing speed, Human research subjects, Hyperbolic functions, Space perception, Graph connectivity
Abstract: We investigate the usability of Euclidean, spherical and hyperbolic geometries for network visualization. Several techniques have been proposed for both spherical and hyperbolic network visualization tools, based on the fact that some networks admit lower embedding error (distortion) in such non‐Euclidean geometries. However, it is not yet known whether a lower embedding error translates to human subject benefits, e.g., better task accuracy or lower task completion time. We design, implement, conduct, and analyze a human subjects study to compare Euclidean, spherical and hyperbolic network visualizations using tasks that span the network task taxonomy. While in some cases accuracy and response times are negatively impacted when using non‐Euclidean visualizations, the evaluation shows that differences in accuracy for hyperbolic and spherical visualizations are not statistically significant when compared to Euclidean visualizations. Additionally, differences in response times for spherical visualizations are not statistically significant compared to Euclidean visualizations. [ABSTRACT FROM AUTHOR]
Copyright of Computer Graphics Forum is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: Euclidean, Hyperbolic, and Spherical Networks: An Empirical Study of Matching Network Structure to Best Visualizations.
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  Data: <searchLink fieldCode="DE" term="%22Euclidean+geometry%22">Euclidean geometry</searchLink><br /><searchLink fieldCode="DE" term="%22Spherical+geometry%22">Spherical geometry</searchLink><br /><searchLink fieldCode="DE" term="%22Task+performance%22">Task performance</searchLink><br /><searchLink fieldCode="DE" term="%22Cognitive+processing+speed%22">Cognitive processing speed</searchLink><br /><searchLink fieldCode="DE" term="%22Human+research+subjects%22">Human research subjects</searchLink><br /><searchLink fieldCode="DE" term="%22Hyperbolic+functions%22">Hyperbolic functions</searchLink><br /><searchLink fieldCode="DE" term="%22Space+perception%22">Space perception</searchLink><br /><searchLink fieldCode="DE" term="%22Graph+connectivity%22">Graph connectivity</searchLink>
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  Data: We investigate the usability of Euclidean, spherical and hyperbolic geometries for network visualization. Several techniques have been proposed for both spherical and hyperbolic network visualization tools, based on the fact that some networks admit lower embedding error (distortion) in such non‐Euclidean geometries. However, it is not yet known whether a lower embedding error translates to human subject benefits, e.g., better task accuracy or lower task completion time. We design, implement, conduct, and analyze a human subjects study to compare Euclidean, spherical and hyperbolic network visualizations using tasks that span the network task taxonomy. While in some cases accuracy and response times are negatively impacted when using non‐Euclidean visualizations, the evaluation shows that differences in accuracy for hyperbolic and spherical visualizations are not statistically significant when compared to Euclidean visualizations. Additionally, differences in response times for spherical visualizations are not statistically significant compared to Euclidean visualizations. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Computer Graphics Forum is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1111/cgf.70126
    Languages:
      – Code: eng
        Text: English
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        PageCount: 12
        StartPage: 1
    Subjects:
      – SubjectFull: Euclidean geometry
        Type: general
      – SubjectFull: Spherical geometry
        Type: general
      – SubjectFull: Task performance
        Type: general
      – SubjectFull: Cognitive processing speed
        Type: general
      – SubjectFull: Human research subjects
        Type: general
      – SubjectFull: Hyperbolic functions
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      – SubjectFull: Space perception
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      – SubjectFull: Graph connectivity
        Type: general
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      – TitleFull: Euclidean, Hyperbolic, and Spherical Networks: An Empirical Study of Matching Network Structure to Best Visualizations.
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            NameFull: Miller, Jacob
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            NameFull: Bhatia, Dhruv
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            NameFull: Purchase, Helen
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              M: 06
              Text: Jun2025
              Type: published
              Y: 2025
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