Graph Embedding Design

Graph Embedding Design is a methodological framework within Knowledge Graph Design that formally specifies the construction and optimization of low-dimensional vector representations of graph entities and relations, distinguished by its focus on preserving structural and semantic properties of the knowledge graph for downstream machine learning tasks.

50

Publications

5.3K

Citations

182

Authors

82

Institutions

Publications per year

2017–2026

36

Authors

182

  1. LG
    Li Guo

    Chinese Academy of Sciences, China

    3 publications · 2.9K citations

    3

    h-index

  2. QY
    Quanming Yao

    Hong Kong University of Science and Technology, Hong Kong SAR China

    3 publications · 121 citations

    3

    h-index

  3. QW
    Quan Wang

    Chinese Academy of Sciences, China

    3 publications · 2.9K citations

    3

    h-index

  4. DF
    Donatella Firmani

    Roma Tre University, Italy

    2 publications · 617 citations

    2

    h-index

  5. PM
    Paolo Merialdo

    Roma Tre University, Italy

    2 publications · 617 citations

    2

    h-index

1–5 of 182

Venues

  1. arXiv (Cornell University)

    Repository · 4 publications · 212 citations

    4

    h-index

  2. Pattern Recognition

    4 publications · 366 citations

    4

    h-index

  3. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 2 publications · 95 citations

    2

    h-index

  4. Knowledge-Based Systems

    2 publications · 63 citations

    2

    h-index

  5. ACM Transactions on Knowledge Discovery from Data

    TKDD · 1 publications · 476 citations

    1

    h-index

1–5