Graph Signal Processing

Graph Signal Processing is a mathematical framework that extends classical signal processing techniques to data structured by graphs, enabling the analysis and manipulation of signals defined on the vertices of a graph according to their underlying connectivity patterns.

537

Publications

35.4K

Citations

963

Authors

422

Institutions

Publications per year

2017–2026

377

Authors

963

  1. AO
    Antonio Ortega

    University of Southern California, United States

    41 publications · 9.3K citations

    27

    h-index

  2. AR
    Alejandro Ribeiro

    University of Pennsylvania, United States

    30 publications · 2.7K citations

    19

    h-index

  3. GL
    Geert Leus

    Delft University of Technology, Netherlands

    33 publications · 2K citations

    19

    h-index

  4. AG
    Antonio G. Marqués

    Universidad Rey Juan Carlos, Spain

    29 publications · 2.1K citations

    19

    h-index

  5. JM
    José M. F. Moura

    Carnegie Mellon University, United States

    22 publications · 4.9K citations

    18

    h-index

1–5 of 963

Venues

  1. IEEE Transactions on Signal Processing

    83 publications · 10K citations

    49

    h-index

  2. IEEE Signal Processing Magazine

    21 publications · 7.1K citations

    19

    h-index

  3. arXiv (Cornell University)

    Repository · 37 publications · 1.5K citations

    18

    h-index

  4. 18

    h-index

  5. IEEE Signal Processing Letters

    22 publications · 757 citations

    15

    h-index

1–5