Dynamic Graph Mining

Dynamic Graph Mining is a subfield of graph mining that focuses on extracting patterns, structures, and knowledge from graphs that evolve over time by explicitly modeling temporal dynamics and structural changes.

103

Publications

5.1K

Citations

342

Authors

170

Institutions

Publications per year

2017–2026

45

Authors

342

  1. LB
    Lawrence B. Holder

    The University of Texas at Arlington, United States

    5 publications · 123 citations

    5

    h-index

  2. LC
    Lei Chen

    Hong Kong University of Science and Technology, Hong Kong SAR China

    3 publications · 89 citations

    3

    h-index

  3. RA
    Rezwan Ahmed

    University of Minnesota, United States

    3 publications · 86 citations

    3

    h-index

  4. CF
    Christos Faloutsos

    Carnegie Mellon University, United States

    3 publications · 613 citations

    3

    h-index

  5. JX
    Jeffrey Xu Yu

    Chinese University of Hong Kong, Hong Kong SAR China

    3 publications · 106 citations

    3

    h-index

1–5 of 342

Venues

  1. Proceedings of the VLDB Endowment

    PVLDB · 5 publications · 272 citations

    5

    h-index

  2. Knowledge-Based Systems

    5 publications · 618 citations

    5

    h-index

  3. Expert Systems with Applications

    4 publications · 198 citations

    4

    h-index

  4. ACM Transactions on Knowledge Discovery from Data

    TKDD · 4 publications · 77 citations

    4

    h-index

  5. Knowledge and Information Systems

    4 publications · 176 citations

    4

    h-index

1–5