Edge Embeddings

Edge Embeddings is a representation learning technique that captures the structural and semantic characteristics of edges in a graph by mapping them into low-dimensional vector spaces, distinguishing itself from node or graph-level embeddings by explicitly modeling pairwise relationships between vertices.

187

Publications

7.5K

Citations

661

Authors

277

Institutions

Publications per year

2017–2026

107

Authors

661

  1. RA
    Ryan A. Rossi

    Adobe Systems (United States), United States

    5 publications · 224 citations

    5

    h-index

  2. NK
    Nesreen K. Ahmed

    Intel (United States), United States

    4 publications · 198 citations

    4

    h-index

  3. SK
    Sungchul Kim

    Adobe Systems (United States), United States

    4 publications · 132 citations

    4

    h-index

  4. DJ
    Di Jin

    Tianjin University, China

    4 publications · 125 citations

    4

    h-index

  5. ER
    Edwin R. Hancock

    University of York, United Kingdom

    4 publications · 125 citations

    4

    h-index

1–5 of 661

Venues

  1. arXiv (Cornell University)

    Repository · 16 publications · 658 citations

    12

    h-index

  2. Neurocomputing

    11 publications · 313 citations

    11

    h-index

  3. Journal of Combinatorial Theory Series B

    9 publications · 417 citations

    9

    h-index

  4. Pattern Recognition

    9 publications · 457 citations

    9

    h-index

  5. Information Sciences

    6 publications · 160 citations

    6

    h-index

1–5