Centrality-based Features

Centrality-Based Features is a graph feature engineering method that quantifies the structural importance of nodes or edges within a network by leveraging centrality measures such as degree, betweenness, closeness, or eigenvector centrality to differentiate the relative influence of entities in the graph topology.

50

Publications

2.2K

Citations

167

Authors

108

Institutions

Publications per year

2017–2026

26

Authors

167

  1. GR
    Giorgio Roffo

    University of Verona, Italy

    2 publications · 44 citations

    2

    h-index

  2. SM
    Simone Melzi

    University of Verona, Italy

    2 publications · 44 citations

    2

    h-index

  3. DJ
    Desmond J. Higham

    University of Strathclyde, United Kingdom

    2 publications · 53 citations

    2

    h-index

  4. VN
    Vanni Noferini

    University of Essex, United Kingdom

    1 publications · 36 citations

    1

    h-index

  5. AZ
    Anmin Zhou

    Sichuan University, China

    1 publications · 73 citations

    1

    h-index

1–5 of 167

Venues

  1. IEEE Access

    3 publications · 131 citations

    3

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 2 publications · 63 citations

    2

    h-index

  3. Knowledge-Based Systems

    2 publications · 60 citations

    2

    h-index

  4. 1

    h-index

  5. Educational Sciences Theory & Practice

    1 publications · 20 citations

    1

    h-index

1–5