Markovian Network Models

Markovian Network Models is a stochastic network model characterized by the Markov property, wherein the future state of the network evolves dependently only on the current state and not on the sequence of preceding states, thereby enabling tractable probabilistic analysis of dynamic network behaviors.

268

Publications

13.8K

Citations

664

Authors

352

Institutions

Publications per year

2017–2026

53

Authors

664

  1. PR
    Philippe Robert

    Institut national de recherche en sciences et technologies du numérique, France

    4 publications · 54 citations

    4

    h-index

  2. AD
    Aniruddha Datta

    Texas A&M University, United States

    3 publications · 239 citations

    3

    h-index

  3. AM
    Ali Movaghar

    Sharif University of Technology, Iran

    3 publications · 271 citations

    3

    h-index

  4. MM
    Michela Meo

    Politecnico di Torino, Italy

    3 publications · 188 citations

    3

    h-index

  5. JF
    Jian‐an Fang

    Donghua University, China

    3 publications · 109 citations

    3

    h-index

1–5 of 664

Venues

  1. Neurocomputing

    8 publications · 148 citations

    8

    h-index

  2. 6

    h-index

  3. arXiv (Cornell University)

    Repository · 6 publications · 125 citations

    6

    h-index

  4. Advances in Applied Probability

    AAP · 6 publications · 142 citations

    6

    h-index

  5. European Journal of Operational Research

    6 publications · 189 citations

    6

    h-index

1–5