Generative Data Visualization

Generative Data Visualization is a computational method that belongs to the broader class of data visualization techniques, distinguished by its capacity to algorithmically produce visual forms through generative processes informed by data inputs.

19

Publications

909

Citations

68

Authors

28

Institutions

Publications per year

2017–2026

7

Authors

68

  1. IT
    Ian T. Nabney

    Aston University, United Kingdom

    4 publications · 188 citations

    4

    h-index

  2. AV
    Alexandre Varnek

    Université de Strasbourg, France

    3 publications · 230 citations

    3

    h-index

  3. PT
    Peter Tiňo

    University of Birmingham, United Kingdom

    3 publications · 142 citations

    3

    h-index

  4. DM
    Dharmesh M. Maniyar

    Aston University, United Kingdom

    2 publications · 73 citations

    2

    h-index

  5. HA
    Héléna A. Gaspar

    King's College London, United Kingdom

    2 publications · 195 citations

    2

    h-index

1–5 of 68

Venues

  1. Journal of Chemical Information and Modeling

    JCIM · 2 publications · 117 citations

    2

    h-index

  2. Molecular Informatics

    2 publications · 170 citations

    2

    h-index

  3. IEEE Computer Graphics and Applications

    1 publications · 46 citations

    1

    h-index

  4. Industrial & Engineering Chemistry Research

    1 publications · 20 citations

    1

    h-index

  5. 1

    h-index

1–5