Graph Anomaly Detection

Graph Anomaly Detection is a computational task within graph analytics that identifies rare, irregular, or suspicious patterns in graph-structured data that deviate significantly from expected structural or behavioral norms.

381

Publications

23.7K

Citations

1.2K

Authors

431

Institutions

Publications per year

2017–2026

232

Authors

1.2K

  1. CF
    Christos Faloutsos

    Carnegie Mellon University, United States

    32 publications · 4K citations

    26

    h-index

  2. LA
    Leman Akoglu

    Carnegie Mellon University, United States

    12 publications · 2.3K citations

    12

    h-index

  3. WE
    William Eberle

    Tennessee Technological University, United States

    10 publications · 499 citations

    10

    h-index

  4. BH
    Bryan Hooi

    National University of Singapore, Singapore

    9 publications · 539 citations

    9

    h-index

  5. LB
    Lawrence B. Holder

    The University of Texas at Arlington, United States

    8 publications · 449 citations

    8

    h-index

1–5 of 1.2K

Venues

  1. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 15 publications · 740 citations

    14

    h-index

  2. arXiv (Cornell University)

    Repository · 19 publications · 605 citations

    13

    h-index

  3. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 11 publications · 475 citations

    10

    h-index

  4. 9

    h-index

  5. IEEE Transactions on Signal Processing

    7 publications · 561 citations

    7

    h-index

1–5