Graph Feature Construction

Graph Feature Construction is a feature engineering methodology that specifically involves the systematic derivation of informative and discriminative attributes from graph-structured data by leveraging topological, structural, and semantic properties inherent to graphs, thereby enabling enhanced machine learning model performance on graph-related tasks.

116

Publications

11.2K

Citations

473

Authors

210

Institutions

Publications per year

2017–2026

75

Authors

473

  1. LD
    Luc De Raedt

    KU Leuven, Belgium

    3 publications · 95 citations

    3

    h-index

  2. BR
    Benedek Rózemberczki

    University of Edinburgh, United Kingdom

    2 publications · 154 citations

    2

    h-index

  3. JZ
    Jun Zhang

    Tencent (China), China

    2 publications · 76 citations

    2

    h-index

  4. EG
    Edward Grefenstette

    University of Oxford, United Kingdom

    2 publications · 4K citations

    2

    h-index

  5. ZF
    Zunlei Feng

    Zhejiang University, China

    2 publications · 90 citations

    2

    h-index

1–5 of 473

Venues

  1. Neurocomputing

    9 publications · 303 citations

    9

    h-index

  2. arXiv (Cornell University)

    Repository · 6 publications · 815 citations

    6

    h-index

  3. Knowledge-Based Systems

    3 publications · 117 citations

    3

    h-index

  4. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 3 publications · 303 citations

    3

    h-index

  5. Expert Systems with Applications

    3 publications · 54 citations

    3

    h-index

1–5