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Multidimensional diffusion processes in dynamic online networks.

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Summary
This summary is machine-generated.

We developed a new algorithm to accurately measure peer influence versus shared preferences in dynamic networks. This method corrects for overestimating influence when common preferences drive adoption.

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Area of Science:

  • Social network analysis
  • Computational social science
  • Machine learning applications

Background:

  • Understanding item adoption in dynamic networks is complex.
  • Distinguishing peer influence from homophily (shared preferences) is a key challenge.
  • Simultaneous diffusion of multiple items complicates analysis.

Purpose of the Study:

  • To develop a dynamic matched sample estimation algorithm.
  • To accurately differentiate peer influence from homophily in item adoption.
  • To address the overestimation of peer influence caused by ignoring common preferences.

Main Methods:

  • Inferring agent preferences using a machine learning algorithm on past adoption data.
  • Matching agents based on inferred preferences for dynamic network analysis.
  • Comparing a novel matching-on-preferences algorithm with other matching strategies.

Main Results:

  • Ignoring previous adoption decisions significantly overestimates peer influence.
  • The machine learning-based matching-on-preferences algorithm substantially reduces the estimated effect of peer influence.
  • This method is more effective than matching on prior adoption or observable characteristics.
  • Significant and intuitive heterogeneity in peer influence effects was observed.

Conclusions:

  • Common preferences are often mistaken for peer influence in diffusion models.
  • Accurate preference inference is crucial for correctly assessing social influence.
  • The developed algorithm provides a more precise understanding of diffusion dynamics in complex networks.