Graph Abstraction

Graph Abstraction is a methodological framework in graph theory and data visualization that reduces the complexity of large networks by hierarchically summarizing structural information, preserving essential topological properties while enabling scalable exploration and analysis.

156

Publications

10.7K

Citations

487

Authors

218

Institutions

Publications per year

2017–2026

46

Authors

487

  1. LS
    Lei Shi

    Chinese Academy of Sciences, China

    4 publications · 84 citations

    4

    h-index

  2. AR
    Arend Rensink

    University of Twente, Netherlands

    3 publications · 40 citations

    3

    h-index

  3. YH
    Yifan Hu

    AT&T (United States), United States

    3 publications · 70 citations

    3

    h-index

  4. FL
    Feng Li

    Indiana University – Purdue University Indianapolis, United States

    3 publications · 80 citations

    3

    h-index

  5. TG
    Tianchong Gao

    Indiana University – Purdue University Indianapolis, United States

    3 publications · 80 citations

    3

    h-index

1–5 of 487

Venues

  1. IEEE Transactions on Visualization and Computer Graphics

    TVCG · 6 publications · 312 citations

    6

    h-index

  2. Computer Graphics Forum

    5 publications · 249 citations

    5

    h-index

  3. arXiv (Cornell University)

    Repository · 3 publications · 113 citations

    3

    h-index

  4. International Joint Conference on Artificial Intelligence

    Conference · 2 publications · 79 citations

    2

    h-index

  5. Journal of Artificial Intelligence Research

    JAIR · 2 publications · 246 citations

    2

    h-index

1–5