Distributed Graph Processing

Distributed Graph Processing is a computational framework within distributed memory systems that enables the parallel execution of graph algorithms across multiple interconnected processors by partitioning and coordinating graph data and operations to achieve scalable and efficient processing of large-scale graph structures.

459

Publications

32K

Citations

1.3K

Authors

415

Institutions

Publications per year

2017–2026

172

Authors

1.3K

  1. DY
    Da Yan

    University of Alabama at Birmingham, United States

    15 publications · 1.1K citations

    13

    h-index

  2. JC
    James Cheng

    Chinese University of Hong Kong, Hong Kong SAR China

    12 publications · 818 citations

    11

    h-index

  3. RC
    Rong Chen

    Shanghai Jiao Tong University, China

    10 publications · 640 citations

    10

    h-index

  4. HC
    Haibo Chen

    Shanghai Jiao Tong University, China

    9 publications · 609 citations

    9

    h-index

  5. JX
    Jeffrey Xu Yu

    Chinese University of Hong Kong, Hong Kong SAR China

    9 publications · 312 citations

    9

    h-index

1–5 of 1.3K

Venues

  1. Proceedings of the VLDB Endowment

    PVLDB · 43 publications · 3.8K citations

    31

    h-index

  2. arXiv (Cornell University)

    Repository · 23 publications · 661 citations

    15

    h-index

  3. IEEE Transactions on Parallel and Distributed Systems

    TPDS · 17 publications · 570 citations

    15

    h-index

  4. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 10 publications · 330 citations

    10

    h-index

  5. Future Generation Computer Systems

    FGCS · 5 publications · 107 citations

    5

    h-index

1–5