Parallel Graph Data Structures

Parallel Graph Data Structures is a class of data organization methods designed to represent graph-theoretic structures in a manner that enables concurrent access and manipulation by multiple computational processes or threads, distinguished by their explicit support for parallelism in operations such as traversal, subgraph extraction, and dynamic updates.

120

Publications

5.8K

Citations

303

Authors

139

Institutions

Publications per year

2017–2026

28

Authors

303

  1. PN
    Philip N. Klein

    Brown University, United States

    5 publications · 155 citations

    5

    h-index

  2. DA
    David A. Bader

    Georgia Institute of Technology, United States

    4 publications · 117 citations

    4

    h-index

  3. VR
    Vijaya Ramachandran

    The University of Texas at Austin, United States

    3 publications · 178 citations

    3

    h-index

  4. GL
    Gary L. Miller

    Carnegie Mellon University, United States

    3 publications · 212 citations

    3

    h-index

  5. SP
    Shietung Peng

    University of Aizu, Japan

    3 publications · 44 citations

    3

    h-index

1–5 of 303

Venues

  1. Algorithmica

    4 publications · 215 citations

    4

    h-index

  2. Journal of Algorithms

    4 publications · 163 citations

    4

    h-index

  3. Information Processing Letters

    3 publications · 107 citations

    3

    h-index

  4. Discrete Mathematics

    3 publications · 62 citations

    3

    h-index

  5. Communications of the ACM

    2 publications · 609 citations

    2

    h-index

1–5