Graph Stream Processing

Graph Stream Processing is a computational framework within graph analytics that enables the continuous ingestion, analysis, and transformation of dynamically evolving graph data through time-ordered sequences of graph updates or events.

152

Publications

9.4K

Citations

377

Authors

175

Institutions

Publications per year

2017–2026

64

Authors

377

  1. CC
    Charų C. Aggarwal

    IBM (United States), United States

    9 publications · 385 citations

    9

    h-index

  2. PW
    Pinghui Wang

    Xi'an Jiaotong University, China

    5 publications · 87 citations

    5

    h-index

  3. NK
    Nesreen K. Ahmed

    Intel (United States), United States

    5 publications · 412 citations

    5

    h-index

  4. GC
    Graham Cormode

    AT&T (United States), United States

    5 publications · 131 citations

    5

    h-index

  5. CF
    Christos Faloutsos

    Carnegie Mellon University, United States

    5 publications · 706 citations

    5

    h-index

1–5 of 377

Venues

  1. Proceedings of the VLDB Endowment

    PVLDB · 16 publications · 1.2K citations

    15

    h-index

  2. ACM Transactions on Knowledge Discovery from Data

    TKDD · 8 publications · 474 citations

    8

    h-index

  3. arXiv (Cornell University)

    Repository · 7 publications · 135 citations

    7

    h-index

  4. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 6 publications · 163 citations

    6

    h-index

  5. The VLDB Journal

    2 publications · 126 citations

    2

    h-index

1–5