Edge Feature Engineering

Edge Feature Engineering is a methodological framework within graph feature engineering that specifically focuses on constructing, transforming, or augmenting attributes associated with the edges of a graph to enhance the representational power of graph-based machine learning models.

52

Publications

1.8K

Citations

181

Authors

77

Institutions

Publications per year

2017–2026

24

Authors

181

  1. ZS
    Zaifeng Shi

    Tianjin University, China

    1 publications · 35 citations

    1

    h-index

  2. RL
    Ren Li

    Tsinghua University, China

    1 publications · 76 citations

    1

    h-index

  3. JG
    Jianwei Gong

    Beijing Institute of Technology, China

    1 publications · 10 citations

    1

    h-index

  4. CM
    Congjie Mi

    Fudan University, China

    1 publications · 25 citations

    1

    h-index

  5. XC
    Xiaojun Cheng

    Boston University, United States

    1 publications · 15 citations

    1

    h-index

1–5 of 181

Venues

  1. Expert Systems with Applications

    2 publications · 90 citations

    2

    h-index

  2. IEEE Access

    2 publications · 40 citations

    2

    h-index

  3. Information Sciences

    1 publications · 75 citations

    1

    h-index

  4. Neurocomputing

    1 publications · 20 citations

    1

    h-index

  5. Mathematical Problems in Engineering

    MPE · 1 publications · 12 citations

    1

    h-index

1–5