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Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community
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Computing global structural balance in large-scale signed social networks.

Giuseppe Facchetti1, Giovanni Iacono, Claudio Altafini

  • 1International School for Advanced Studies, via Bonomea 265, 34136 Trieste, Italy.

Proceedings of the National Academy of Sciences of the United States of America
|December 15, 2011
PubMed
Summary
This summary is machine-generated.

Online social networks are highly balanced, avoiding conflictual situations. This balance arises from skewed sign distributions, creating apparent disorder rather than true frustration in interactions.

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Area of Science:

  • Social Network Analysis
  • Statistical Physics
  • Computational Social Science

Background:

  • Structural balance theory posits that signed social networks avoid conflictual cycles.
  • Online social networks involve complex interactions among individuals.

Purpose of the Study:

  • To compute the global balance level in large-scale online social networks.
  • To verify if current online networks adhere to structural balance theory.
  • To explain the observed balance in terms of sign distribution skewness.

Main Methods:

  • Utilized an algorithm for ground-state calculation in large-scale Ising spin glasses.
  • Applied computational methods to analyze network structures and edge sign distributions.
  • Quantified the global balance of extensive online social network datasets.

Main Results:

  • Very large online social networks were found to be extremely balanced.
  • The high degree of balance is linked to skewed sign distributions on network nodes.
  • A majority of negative edges per individual leads to apparent disorder, not network frustration.

Conclusions:

  • Online social networks demonstrate a high degree of structural balance.
  • Skewed sign distributions are key to maintaining network balance and avoiding conflict.
  • The findings support structural balance theory in the context of modern digital interactions.