Explainable Visual Analytics

Explainable Visual Analytics is a visual analytics approach that integrates transparent, interpretable, and human-understandable mechanisms into the visual data exploration process to make the reasoning, assumptions, and outcomes of analytical models and algorithms comprehensible and justifiable to users.

38

Publications

2.5K

Citations

141

Authors

57

Institutions

Publications per year

2017–2026

27

Authors

141

  1. EB
    Enrico Bertini

    New York University, United States

    4 publications · 245 citations

    4

    h-index

  2. ME
    Mennatallah El‐Assady

    University of Konstanz, Germany

    4 publications · 342 citations

    4

    h-index

  3. HQ
    Huamin Qu

    Hong Kong University of Science and Technology, Hong Kong SAR China

    3 publications · 76 citations

    3

    h-index

  4. NE
    Niklas Elmqvist

    University of Maryland, College Park, United States

    2 publications · 32 citations

    2

    h-index

  5. FS
    Fabian Sperrle

    University of Konstanz, Germany

    2 publications · 72 citations

    2

    h-index

1–5 of 141

Venues

  1. IEEE Transactions on Visualization and Computer Graphics

    TVCG · 11 publications · 674 citations

    10

    h-index

  2. arXiv (Cornell University)

    Repository · 2 publications · 23 citations

    2

    h-index

  3. IEEE Computer Graphics and Applications

    3 publications · 312 citations

    2

    h-index

  4. Journal of Medical Systems

    1 publications · 61 citations

    1

    h-index

  5. World Wide Web

    1 publications · 37 citations

    1

    h-index

1–5