Spectral Graph Learning

Spectral Graph Learning is a graph-based learning framework that leverages the spectral decomposition of graph operators, such as the graph Laplacian, to extract and exploit low-dimensional representations of graph structure by transforming graph data into a spectral domain where topological patterns are more discernible.

216

Publications

48K

Citations

703

Authors

291

Institutions

Publications per year

2017–2026

132

Authors

703

  1. FN
    Feiping Nie

    Northwestern Polytechnical University, China

    7 publications · 771 citations

    7

    h-index

  2. JH
    Jiawei Han

    University of Illinois Urbana-Champaign, United States

    4 publications · 333 citations

    4

    h-index

  3. HC
    Heng Chang

    Tsinghua University, China

    3 publications · 61 citations

    3

    h-index

  4. JY
    Jian Yang

    Nanjing University of Science and Technology, China

    3 publications · 117 citations

    3

    h-index

  5. SS
    Somayeh Sojoudi

    University of California, Berkeley, United States

    3 publications · 61 citations

    3

    h-index

1–5 of 703

Venues

  1. arXiv (Cornell University)

    Repository · 36 publications · 19.2K citations

    24

    h-index

  2. Neurocomputing

    6 publications · 310 citations

    6

    h-index

  3. Knowledge-Based Systems

    5 publications · 449 citations

    5

    h-index

  4. Information Sciences

    4 publications · 103 citations

    4

    h-index

  5. 4

    h-index

1–5