Tree Visualization Scalability

Tree Visualization Scalability is a methodological framework that enables the effective rendering and interactive exploration of hierarchically structured data as tree visualizations, distinguishing itself by maintaining perceptual clarity and navigational efficiency as the size and complexity of the data increase.

7

Publications

540

Citations

28

Authors

17

Institutions

Publications per year

2017–2026

1

Authors

28

  1. QV
    Quang Vinh Nguyễn

    University of Technology Sydney, Australia

    2 publications · 22 citations

    2

    h-index

  2. SS
    Simeon Simoff

    University of Technology Sydney, Australia

    1 publications · 11 citations

    1

    h-index

  3. МA
    Мarcus A. Koch

    Heidelberg University, Germany

    1 publications · 375 citations

    1

    h-index

  4. KW
    Kent Wittenburg

    Mitsubishi Electric (United States), United States

    1 publications · 15 citations

    1

    h-index

  5. DB
    Dale Beermann

    Research Institute of Precision Instruments (Russia), Russia

    1 publications · 16 citations

    1

    h-index

1–5 of 28

Venues

  1. Eurographics

    Repository · 1 publications · 16 citations

    1

    h-index

  2. Journal Of Big Data

    1 publications · 11 citations

    1

    h-index

  3. Journal of Chemical Information and Modeling

    JCIM · 1 publications · 375 citations

    1

    h-index

  4. IEEE Transactions on Visualization and Computer Graphics

    TVCG · 1 publications · 28 citations

    1

    h-index

  5. Journal of Visual Languages & Computing

    JVLC · 1 publications · 11 citations

    1

    h-index

1–5