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Multidimensional diffusion processes in dynamic online networks

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  • David Easley
  • Eleonora Patacchini
  • Christopher Rojas

Abstract

We develop a dynamic matched sample estimation algorithm to distinguish peer influence and homophily effects on item adoption decisions in dynamic networks, with numerous items diffusing simultaneously. We infer preferences using a machine learning algorithm applied to previous adoption decisions, and we match agents using those inferred preferences. We show that ignoring previous adoption decisions leads to significantly overestimating the role of peer influence in the diffusion of information, mistakenly confounding influence-based contagion with diffusion driven by common preferences. Our matching-on-preferences algorithm with machine learning reduces the relative effect of peer influence on item adoption decisions in this network significantly more than matching on earlier adoption decisions, as well other observable characteristics. We also show significant and intuitive heterogeneity in the relative effect of peer influence.

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  • David Easley & Eleonora Patacchini & Christopher Rojas, 2020. "Multidimensional diffusion processes in dynamic online networks," PLOS ONE, Public Library of Science, vol. 15(2), pages 1-21, February.
    • Handle: RePEc:plo:pone00:0228421
    DOI: 10.1371/journal.pone.0228421
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    1. repec:cup:cbooks:9780511761942 is not listed on IDEAS
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    Cited by:

    1. Patacchini, Eleonora & Hsieh, Chih-Sheng & Lin, Xu, 2019. "Social Interaction Methods," CEPR Discussion Papers 14141, C.E.P.R. Discussion Papers.

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