Graph Transduction

Graph Transduction is a semi-supervised learning method that, within the context of network propagation, leverages both labeled and unlabeled nodes in a graph to infer the labels of unseen nodes by exploiting the graph's structural and feature information.

34

Publications

2.3K

Citations

91

Authors

53

Institutions

Publications per year

2017–2026

12

Variants

Transductive Learning On Graphs

Authors

91

  1. MP
    Marcello Pelillo

    Ca' Foscari University of Venice, Italy

    4 publications · 97 citations

    4

    h-index

  2. SV
    Sebastiano Vascon

    Ca' Foscari University of Venice, Italy

    3 publications · 52 citations

    3

    h-index

  3. KD
    Kevin Duh

    Johns Hopkins University, United States

    2 publications · 120 citations

    2

    h-index

  4. SA
    Sinem Aslan

    Ca' Foscari University of Venice, Italy

    2 publications · 38 citations

    2

    h-index

  5. YZ
    Yingying Zhu

    University of North Carolina at Chapel Hill, United States

    2 publications · 79 citations

    2

    h-index

1–5 of 91

Venues

  1. Pattern Recognition Letters

    3 publications · 53 citations

    3

    h-index

  2. IEEE Transactions on Image Processing

    2 publications · 435 citations

    2

    h-index

  3. arXiv (Cornell University)

    Repository · 2 publications · 48 citations

    2

    h-index

  4. Information Sciences

    2 publications · 58 citations

    2

    h-index

  5. 2

    h-index

1–5