Graph-based Fusion

Graph-Based Fusion is a data integration framework that uses graph structures to model relationships among heterogeneous data sources, distinguishing itself by leveraging node-edge representations to enable joint inference and information propagation across modalities in multi-omics, multisensor, and object detection contexts.

171

Publications

11.3K

Citations

743

Authors

270

Institutions

Publications per year

2017–2026

144

Authors

743

  1. EZ
    En Zhu

    National University of Defense Technology, China

    5 publications · 315 citations

    5

    h-index

  2. JC
    Jocelyn Chanussot

    Institut polytechnique de Grenoble, France

    5 publications · 147 citations

    5

    h-index

  3. WL
    Wenzhi Liao

    Ghent University, Belgium

    4 publications · 270 citations

    4

    h-index

  4. XL
    Xinwang Liu

    National University of Defense Technology, China

    4 publications · 288 citations

    4

    h-index

  5. YW
    Yongxin Wang

    Shandong University, China

    3 publications · 338 citations

    3

    h-index

1–5 of 743

Venues

  1. arXiv (Cornell University)

    Repository · 7 publications · 329 citations

    7

    h-index

  2. Information Fusion

    9 publications · 363 citations

    7

    h-index

  3. Knowledge-Based Systems

    6 publications · 609 citations

    6

    h-index

  4. 6

    h-index

  5. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 5 publications · 118 citations

    5

    h-index

1–5