Graph Outlier Detection

Graph Outlier Detection is an outlier detection paradigm that specifically identifies anomalous instances within graph-structured data by exploiting relational topology and attribute dependencies distinct from conventional feature-based or distance-based outlier detection methods.

160

Publications

10.5K

Citations

477

Authors

211

Institutions

Publications per year

2017–2026

80

Authors

477

  1. LA
    Leman Akoglu

    Carnegie Mellon University, United States

    7 publications · 2.2K citations

    7

    h-index

  2. CL
    Chang‐Tien Lu

    Virginia Tech, United States

    5 publications · 285 citations

    5

    h-index

  3. JH
    Jiawei Han

    University of Illinois Urbana-Champaign, United States

    5 publications · 390 citations

    5

    h-index

  4. CF
    Christos Faloutsos

    Carnegie Mellon University, United States

    5 publications · 412 citations

    5

    h-index

  5. JW
    Jia Wu

    Macquarie University, Australia

    4 publications · 235 citations

    4

    h-index

1–5 of 477

Venues

  1. arXiv (Cornell University)

    Repository · 10 publications · 316 citations

    10

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 8 publications · 425 citations

    8

    h-index

  3. IEEE Transactions on Signal Processing

    4 publications · 163 citations

    4

    h-index

  4. IEEE Access

    3 publications · 67 citations

    3

    h-index

  5. Proceedings of the VLDB Endowment

    PVLDB · 2 publications · 131 citations

    2

    h-index

1–5