Generative Visualization

Generative Visualization is a type of data visualization method that employs algorithmic or computational processes to autonomously create visual representations from data, distinguished by its use of generative models or rules to produce novel, dynamic, or evolving visual forms.

13

Publications

451

Citations

47

Authors

18

Institutions

Publications per year

2017–2026

6

Authors

47

  1. ME
    Mennatallah El‐Assady

    University of Konstanz, Germany

    3 publications · 93 citations

    3

    h-index

  2. SD
    Sara Di Bartolomeo

    Northeastern University, United States

    2 publications · 65 citations

    2

    h-index

  3. AM
    Andrew McNutt

    University of Chicago, United States

    2 publications · 65 citations

    2

    h-index

  4. HB
    Hugo Berard

    Université de Montréal, Canada

    2 publications · 35 citations

    2

    h-index

  5. SL
    Simon Lacoste-Julien

    Université de Montréal, Canada

    2 publications · 35 citations

    2

    h-index

1–5 of 47

Venues

  1. International Conference on Learning Representations

    Conference · 1 publications · 17 citations

    1

    h-index

  2. IEEE Transactions on Visualization and Computer Graphics

    TVCG · 1 publications · 59 citations

    1

    h-index

  3. IEEE Computer Graphics and Applications

    1 publications · 46 citations

    1

    h-index

  4. Visual Informatics

    1 publications · 106 citations

    1

    h-index

  5. arXiv (Cornell University)

    Repository · 1 publications · 18 citations

    1

    h-index

1–5