Spectral Graph Features

Spectral Graph Features is a class of graph-derived metrics that captures structural properties of a graph through the eigenvalue decomposition of its Laplacian or adjacency matrix, distinguishing it from other graph feature types by its foundation in spectral graph theory and its ability to encode global connectivity patterns.

47

Publications

2.4K

Citations

163

Authors

74

Institutions

Publications per year

2017–2026

27

Authors

163

  1. BJ
    Bo Jiao

    Xi'an Jiaotong University, China

    2 publications · 32 citations

    2

    h-index

  2. JS
    Jianmai Shi

    National University of Defense Technology, China

    2 publications · 32 citations

    2

    h-index

  3. JL
    Jean‐Michel Lett

    Centre de Coopération Internationale en Recherche Agronomique pour le Développement, France

    1 publications · 56 citations

    1

    h-index

  4. YC
    Yannan Chen

    Jiangsu University, China

    1 publications · 37 citations

    1

    h-index

  5. KR
    Kannan Ramchandran

    University of California, Berkeley, United States

    1 publications · 37 citations

    1

    h-index

1–5 of 163

Venues

  1. arXiv (Cornell University)

    Repository · 5 publications · 116 citations

    5

    h-index

  2. 1

    h-index

  3. 1

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  4. IEEE Transactions on Image Processing

    1 publications · 60 citations

    1

    h-index

  5. 1

    h-index

1–5