Information Cascade Analysis

Information Cascade Analysis is a computational framework within social network mining that models the propagation dynamics of information across a network by identifying and analyzing the sequence and structure of adoption events to distinguish influence-driven diffusion from independent behavior.

149

Publications

17.6K

Citations

408

Authors

222

Institutions

Publications per year

2017–2026

49

Authors

408

  1. DH
    David Hirshleifer

    National Bureau of Economic Research, United States

    4 publications · 10.5K citations

    5

    h-index

  2. JL
    Jure Leskovec

    Stanford University, United States

    4 publications · 715 citations

    4

    h-index

  3. SB
    Sushil Bikhchandani

    University of California, Los Angeles, United States

    4 publications · 10.5K citations

    4

    h-index

  4. IW
    Ivo Welch

    University of California, Los Angeles, United States

    4 publications · 10.5K citations

    4

    h-index

  5. SM
    Shintaro Mori

    Kitasato University, Japan

    3 publications · 66 citations

    3

    h-index

1–5 of 408

Venues

  1. RePEc: Research Papers in Economics

    Repository · 6 publications · 6.8K citations

    6

    h-index

  2. 5

    h-index

  3. arXiv (Cornell University)

    Repository · 4 publications · 168 citations

    4

    h-index

  4. American Economic Review

    4 publications · 735 citations

    4

    h-index

  5. PLoS ONE

    2 publications · 88 citations

    2

    h-index

1–5