Temporal Network Inference

Temporal Network Inference is a methodological framework within dynamic network analysis that enables the reconstruction or prediction of time-varying network structures from observed temporal data by distinguishing evolving node interactions from static or aggregated representations.

74

Publications

5.3K

Citations

257

Authors

129

Institutions

Publications per year

2017–2026

46

Authors

257

  1. IP
    Ioannis Psorakis

    University of Oxford, United Kingdom

    2 publications · 280 citations

    2

    h-index

  2. YC
    Yen‐Yu Chang

    Stanford University, United States

    2 publications · 65 citations

    2

    h-index

  3. RW
    Reínout W. Wiers

    University of Amsterdam, Netherlands

    2 publications · 183 citations

    2

    h-index

  4. DK
    David Kempe

    University of Southern California, United States

    2 publications · 597 citations

    2

    h-index

  5. YT
    Yuichi Tanaka

    Tokyo University of Agriculture and Technology, Japan

    2 publications · 78 citations

    2

    h-index

1–5 of 257

Venues

  1. arXiv (Cornell University)

    Repository · 6 publications · 206 citations

    6

    h-index

  2. Information Sciences

    3 publications · 193 citations

    3

    h-index

  3. Nature Communications

    2 publications · 145 citations

    2

    h-index

  4. 2

    h-index

  5. Journal of The Royal Society Interface

    2 publications · 251 citations

    2

    h-index

1–5