Hierarchical Data Exploration

Hierarchical Data Exploration is a visual data exploration paradigm that organizes and navigates data through nested structures, enabling users to interactively traverse and analyze relationships across multiple levels of abstraction within hierarchical datasets.

77

Publications

4.4K

Citations

256

Authors

121

Institutions

Publications per year

2017–2026

19

Authors

256

  1. BS
    Ben Shneiderman

    University of Maryland, College Park, United States

    3 publications · 255 citations

    3

    h-index

  2. SC
    Sheelagh Carpendale

    University of Calgary, Canada

    2 publications · 138 citations

    2

    h-index

  3. IV
    Ivan Viola

    TU Wien, Austria

    2 publications · 37 citations

    2

    h-index

  4. ES
    Edward Seidel

    National Center for Supercomputing Applications, United States

    2 publications · 33 citations

    2

    h-index

  5. IT
    Ian T. Nabney

    Aston University, United Kingdom

    2 publications · 115 citations

    2

    h-index

1–5 of 256

Venues

  1. IEEE Transactions on Visualization and Computer Graphics

    TVCG · 21 publications · 1.3K citations

    17

    h-index

  2. IEEE Computer Graphics and Applications

    2 publications · 128 citations

    2

    h-index

  3. bioRxiv (Cold Spring Harbor Laboratory)

    Repository · 2 publications · 36 citations

    2

    h-index

  4. Neural Networks

    2 publications · 77 citations

    2

    h-index

  5. Eurographics

    Repository · 2 publications · 112 citations

    2

    h-index

1–5