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Structural diversity in social contagion.

Johan Ugander1, Lars Backstrom, Cameron Marlow

  • 1Center for Applied Mathematics and Department of Computer Science, Cornell University, Ithaca, NY 14853, USA.

Proceedings of the National Academy of Sciences of the United States of America
|April 5, 2012
PubMed
Summary
This summary is machine-generated.

Social contagion, like the spread of fads or opinions, is driven by network structure, not just neighborhood size. Analyzing Facebook data reveals "structural diversity" is key to understanding contagion spread.

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Area of Science:

  • Social network analysis
  • Computational social science
  • Epidemiology

Background:

  • Social contagion models traditionally rely on the "contact neighborhood hypothesis," assuming spread probability increases with the number of contacts.
  • Evaluating this hypothesis is difficult due to data limitations on individual network structures during contagion events.

Purpose of the Study:

  • To investigate the factors influencing social contagion by analyzing large-scale network data.
  • To challenge traditional models by examining the role of network structure beyond simple neighborhood size.

Main Methods:

  • Analysis of the global adoption and growth patterns of the Facebook social network.
  • Quantification of individual "contact neighborhoods" and their structural properties.

Main Results:

  • Contagion probability is primarily determined by the "structural diversity" (number of connected components) within an individual's neighborhood.
  • Neighborhood size, after controlling for structural diversity, often negatively predicts contagion.
  • Large-scale network data reveals subtle structural signals critical for predicting social process outcomes.

Conclusions:

  • Social contagion dynamics are more nuanced than previously modeled, emphasizing network topology over sheer contact numbers.
  • The findings highlight the importance of "structural diversity" in understanding information and behavior spread across social networks.
  • Large-scale data analysis enables the discovery of previously undetected network effects crucial for social processes.