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  • September 16, 2022
  • Article
  • Science

A Causal Test of the Strength of Weak Ties

By: Karthik Rajkumar, Guillaume Saint-Jacques, Iavor I. Bojinov, Erik Brynjolfsson and Sinan Aral
  • Format:Print
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Abstract

The authors analyzed data from multiple large-scale randomized experiments on LinkedIn’s People You May Know algorithm, which recommends new connections to LinkedIn members, to test the extent to which weak ties increased job mobility in the world’s largest professional social network. The experiments randomly varied the prevalence of weak ties in the networks of over 20 million people over a 5-year period, during which 2 billion new ties and 600,000 new jobs were created. The results provided experimental causal evidence supporting the strength of weak ties and suggested three revisions to the theory. First, the strength of weak ties was nonlinear. Statistical analysis found an inverted U-shaped relationship between tie strength and job transmission such that weaker ties increased job transmission but only to a point, after which there were diminishing marginal returns to tie weakness. Second, weak ties measured by interaction intensity and the number of mutual connections displayed varying effects. Moderately weak ties (measured by mutual connections) and the weakest ties (measured by interaction intensity) created the most job mobility. Third, the strength of weak ties varied by industry. Whereas weak ties increased job mobility in more digital industries, strong ties increased job mobility in less digital industries.

Keywords

Job Mobility; Social Networks; Social Ties; Networks; Personal Development and Career

Citation

Rajkumar, Karthik, Guillaume Saint-Jacques, Iavor I. Bojinov, Erik Brynjolfsson, and Sinan Aral. "A Causal Test of the Strength of Weak Ties." Science 377, no. 6612 (September 16, 2022).
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About The Author

Iavor I. Bojinov

Technology and Operations Management
→More Publications

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More from the Authors
  • Nailing Prediction: Experimental Evidence on the Value of Tools in Predictive Model Development By: Daniel Yue, Paul Hamilton and Iavor Bojinov
  • On Ramp to Crypto By: Iavor Bojinov, Michael Parzen and Paul Hamilton
  • Data Privacy in Practice at LinkedIn By: Iavor Bojinov, Marco Iansiti and Seth Neel
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