Node Feature Engineering

Node Feature Engineering is a methodological framework within graph feature engineering that specifically involves the systematic design, transformation, and augmentation of attribute data associated with individual nodes to enhance the representational quality and predictive performance of graph-based machine learning models.

28

Publications

12K

Citations

112

Authors

56

Institutions

Publications per year

2017–2026

21

Authors

112

  1. JL
    Jure Leskovec

    Stanford University, United States

    4 publications · 11.1K citations

    4

    h-index

  2. MK
    Myunghwan Kim

    Stanford University, United States

    2 publications · 56 citations

    2

    h-index

  3. XR
    Xiaobin Rui

    China University of Mining and Technology, China

    1 publications · 43 citations

    1

    h-index

  4. JG
    Jun Gao

    Peking University, China

    1 publications · 17 citations

    1

    h-index

  5. YC
    Yang Chen

    Qinghai Normal University, China

    1 publications · 17 citations

    1

    h-index

1–5 of 112

Venues

  1. IEEE Access

    4 publications · 179 citations

    4

    h-index

  2. arXiv (Cornell University)

    Repository · 3 publications · 128 citations

    3

    h-index

  3. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 2 publications · 152 citations

    2

    h-index

  4. International Journal of Intelligent Systems

    2 publications · 41 citations

    2

    h-index

  5. Computer Communications

    1 publications · 20 citations

    1

    h-index

1–5