Spectral Graph Algorithms

Spectral Graph Algorithms is a class of graph algorithms that leverage the eigenvalues and eigenvectors of matrices associated with a graph—such as the adjacency or Laplacian matrix—to analyze and solve problems related to graph structure, partitioning, and embedding.

511

Publications

41.8K

Citations

1.1K

Authors

526

Institutions

Publications per year

2017–2026

145

Authors

1.1K

  1. DA
    Daniel A. Spielman

    Yale University, United States

    12 publications · 2.2K citations

    11

    h-index

  2. RP
    Richard Peng

    Georgia Institute of Technology, United States

    12 publications · 373 citations

    11

    h-index

  3. ST
    Shang‐Hua Teng

    University of Southern California, United States

    9 publications · 1K citations

    9

    h-index

  4. FN
    Feiping Nie

    Northwestern Polytechnical University, China

    8 publications · 924 citations

    8

    h-index

  5. PR
    Prahalada Rao

    University of Nebraska–Lincoln, United States

    7 publications · 347 citations

    7

    h-index

1–5 of 1.1K

Venues

  1. Linear Algebra and its Applications

    53 publications · 1.7K citations

    24

    h-index

  2. arXiv (Cornell University)

    Repository · 22 publications · 567 citations

    15

    h-index

  3. Discrete Mathematics

    13 publications · 676 citations

    11

    h-index

  4. SIAM Journal on Computing

    9 publications · 1.7K citations

    9

    h-index

  5. 7

    h-index

1–5