Feature Augmentation

Feature Augmentation is a graph feature engineering technique that expands the original node or edge features by introducing derived or synthetic attributes through deterministic or learned transformations to enhance representational capacity.

346

Publications

35.9K

Citations

1.4K

Authors

471

Institutions

Publications per year

2017–2026

321

Authors

1.4K

  1. BH
    Bryan Hooi

    National University of Singapore, Singapore

    5 publications · 357 citations

    5

    h-index

  2. LN
    Loris Nanni

    University of Bologna, Italy

    5 publications · 207 citations

    5

    h-index

  3. MP
    Michelangelo Paci

    Tampere University, Finland

    5 publications · 207 citations

    5

    h-index

  4. SB
    Sheryl Brahnam

    Missouri State University, United States

    4 publications · 193 citations

    4

    h-index

  5. ZW
    Zuxuan Wu

    Fudan University, China

    3 publications · 174 citations

    3

    h-index

1–5 of 1.4K

Venues

  1. arXiv (Cornell University)

    Repository · 37 publications · 2.6K citations

    23

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 11 publications · 598 citations

    10

    h-index

  3. IEEE Transactions on Image Processing

    9 publications · 673 citations

    9

    h-index

  4. Pattern Recognition

    8 publications · 389 citations

    8

    h-index

  5. IEEE Access

    8 publications · 625 citations

    8

    h-index

1–5