State-space Network Models

State-Space Network Models is a dynamic network modeling framework that represents the evolution of network structures over time by specifying a latent state space process governing network transitions, distinguished from static or event-based models by its explicit separation of latent state dynamics and observable network outputs.

146

Publications

6.7K

Citations

431

Authors

231

Institutions

Publications per year

2017–2026

53

Authors

431

  1. SW
    Sean Warnick

    Brigham Young University, United States

    3 publications · 55 citations

    3

    h-index

  2. JH
    Jun Hu

    Harbin University of Science and Technology, China

    3 publications · 279 citations

    3

    h-index

  3. ZW
    Zidong Wang

    Brunel University of London, United Kingdom

    3 publications · 327 citations

    3

    h-index

  4. FE
    Fuad E. Alsaadi

    King Abdulaziz University, Saudi Arabia

    3 publications · 180 citations

    3

    h-index

  5. YL
    Yurong Liu

    Yangzhou University, China

    3 publications · 157 citations

    3

    h-index

1–5 of 431

Venues

  1. Neurocomputing

    10 publications · 379 citations

    10

    h-index

  2. IEEE Transactions on Automatic Control

    5 publications · 456 citations

    4

    h-index

  3. Journal of the Franklin Institute

    4 publications · 100 citations

    4

    h-index

  4. Neural Networks

    3 publications · 126 citations

    3

    h-index

  5. Automatica

    3 publications · 238 citations

    3

    h-index

1–5