Graph Pattern Mining

Graph Pattern Mining is a computational method that identifies recurring and significant subgraph structures within graph datasets to uncover hidden relational patterns, distinguishing it from broader graph analysis by focusing specifically on the discovery of frequent, meaningful, and potentially predictive graph motifs.

326

Publications

20.6K

Citations

833

Authors

375

Institutions

Publications per year

2017–2026

89

Authors

833

  1. XY
    Xifeng Yan

    University of California, Santa Barbara, United States

    14 publications · 3.8K citations

    14

    h-index

  2. WF
    Wenfei Fan

    University of Edinburgh, United Kingdom

    14 publications · 1.3K citations

    14

    h-index

  3. YW
    Yinghui Wu

    Washington State University, United States

    12 publications · 1.1K citations

    12

    h-index

  4. JH
    Jiawei Han

    University of Illinois Urbana-Champaign, United States

    9 publications · 1.6K citations

    9

    h-index

  5. GK
    George Karypis

    University of Minnesota, United States

    8 publications · 2K citations

    8

    h-index

1–5 of 833

Venues

  1. Proceedings of the VLDB Endowment

    PVLDB · 15 publications · 1.5K citations

    15

    h-index

  2. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 15 publications · 793 citations

    13

    h-index

  3. Knowledge and Information Systems

    12 publications · 324 citations

    11

    h-index

  4. Data Mining and Knowledge Discovery

    10 publications · 629 citations

    10

    h-index

  5. Information Sciences

    6 publications · 251 citations

    6

    h-index

1–5