Graph-based Detection

Graph-Based Detection is a detection method that utilizes graph structures to model and analyze relationships among entities, distinguishing itself by identifying suspicious patterns and anomalous behaviors indicative of money laundering through the topological analysis of financial transaction networks.

280

Publications

14.8K

Citations

998

Authors

388

Institutions

Publications per year

2017–2026

184

Authors

998

  1. CF
    Christos Faloutsos

    Carnegie Mellon University, United States

    12 publications · 1.5K citations

    12

    h-index

  2. AB
    Alex Beutel

    Google (United States), United States

    6 publications · 713 citations

    6

    h-index

  3. JW
    Jia Wu

    Macquarie University, Australia

    5 publications · 503 citations

    5

    h-index

  4. ZL
    Zhao Li

    Alibaba Group (China), China

    5 publications · 275 citations

    5

    h-index

  5. LB
    Lawrence B. Holder

    The University of Texas at Arlington, United States

    5 publications · 325 citations

    5

    h-index

1–5 of 998

Venues

  1. arXiv (Cornell University)

    Repository · 8 publications · 203 citations

    8

    h-index

  2. IEEE Access

    6 publications · 302 citations

    6

    h-index

  3. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 6 publications · 305 citations

    6

    h-index

  4. Expert Systems with Applications

    5 publications · 341 citations

    5

    h-index

  5. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 5 publications · 250 citations

    4

    h-index

1–5