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Synthesis of Cyclic Polymers and Characterization of Their Diffusive Motion in the Melt State at the Single Molecule Level
Published on: September 26, 2016
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Multidimensional diffusion processes in dynamic online networks
David Easley1, Eleonora Patacchini1, Christopher Rojas1
1Department of Economics, Cornell University, Ithaca, NY, United States of America.
Plos One
|February 7, 2020
Summary
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.
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.
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