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Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community
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Inferring signed social networks from contact patterns.

Dávid Ferenczi1, Jean-Gabriel Young2,3, Leto Peel1

  • 1Department of Data Analytics and Digitalisation, School of Business and Economics, Maastricht University, 6211 LM Maastricht, The Netherlands.

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PubMed
Summary
This summary is machine-generated.

This study introduces a Bayesian framework to differentiate between absent relationships and negative ties in social networks based on contact patterns. The method accurately identifies negative connections, improving social network analysis.

Keywords:
Bayesian inferencenetwork reconstructionsigned networks

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Area of Science:

  • Network Science
  • Computational Social Science
  • Statistical Inference

Background:

  • Inferring social networks often relies on indirect data like proximity.
  • Existing methods struggle to distinguish between no observed interactions due to lack of opportunity versus active avoidance (negative ties).

Purpose of the Study:

  • To develop a method for inferring signed social networks from contact patterns.
  • To differentiate between absent relationships and actual negative ties when no interactions are observed.

Main Methods:

  • A Bayesian framework utilizing Markov Chain Monte Carlo (MCMC) inference.
  • Modeling interaction groups to distinguish chance encounters from deliberate avoidance.
  • Validation using synthetic data and application to real-world high school contact data.

Main Results:

  • The proposed method significantly outperforms baseline approaches, especially in detecting negative edges.
  • Analysis of French high school data revealed a network structure aligning with friendship survey findings.
  • Posterior predictive checks confirmed the model's robustness and adequacy.

Conclusions:

  • The developed Bayesian framework effectively infers signed social networks by distinguishing between lack of opportunity and negative relationships.
  • This approach offers a more nuanced understanding of social network structures, crucial for fields like sociology and epidemiology.