Virality Modeling

Virality Modeling is a computational framework that quantifies and predicts the likelihood and extent of content dissemination across social networks by integrating propagation dynamics, user engagement patterns, and network topology to distinguish mechanisms driving organic reach amplification.

117

Publications

14.4K

Citations

326

Authors

161

Institutions

Publications per year

2017–2026

47

Authors

326

  1. JL
    Jure Leskovec

    Stanford University, United States

    5 publications · 2.7K citations

    5

    h-index

  2. LA
    Lada A. Adamic

    University of Michigan, United States

    5 publications · 2.6K citations

    5

    h-index

  3. EL
    Ee‐Peng Lim

    Singapore Management University, Singapore

    4 publications · 94 citations

    4

    h-index

  4. LX
    Lexing Xie

    Australian National University, Australia

    4 publications · 149 citations

    4

    h-index

  5. MR
    Marian-Andrei Rizoiu

    Australian National University, Australia

    4 publications · 149 citations

    4

    h-index

1–5 of 326

Venues

  1. 9

    h-index

  2. arXiv (Cornell University)

    Repository · 3 publications · 84 citations

    3

    h-index

  3. Expert Systems with Applications

    3 publications · 152 citations

    3

    h-index

  4. Journal of Intelligent Information Systems

    2 publications · 113 citations

    2

    h-index

  5. 2

    h-index

1–5