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Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community
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Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community

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Multilayer weighted social network model.

Yohsuke Murase1, János Török2, Hang-Hyun Jo3

  • 1RIKEN Advanced Institute for Computational Science, 7-1-26, Minatojima-minami-machi, Chuo-ku, Kobe, Hyogo, 650-0047, Japan and CREST, Japan Science and Technology Agency 4-1-8 Honcho, Kawaguchi, Saitama, 332-0012, Japan.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|December 11, 2014
PubMed
Summary
This summary is machine-generated.

This study models complex social networks, showing how geographic constraints in multilayer weighted social networks (WSN) preserve community structures while increasing overlap. This reveals a more realistic societal structure.

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Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community
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Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community

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Area of Science:

  • Social Network Analysis
  • Sociology
  • Computational Social Science

Background:

  • Empirical studies confirm Granovetter's hypothesis of strong communities linked by weak ties.
  • Individual interactions across diverse contexts lead to overlapping communities.
  • This suggests a multilayered societal structure where layers represent different contexts.

Purpose of the Study:

  • To model multilayered social networks that incorporate Granovetterian structure and community overlap.
  • To investigate the impact of interlayer correlation on network topology and community overlap.
  • To develop a model that preserves Granovetterian structure while enhancing community overlap.

Main Methods:

  • Developed a single-layer weighted social network (WSN) model.
  • Merged WSN models to study interlayer correlation effects.
  • Devised a geographic multilayer WSN model incorporating geographic constraints.

Main Results:

  • Merging WSN models requires significant interlayer correlation to maintain topology-link weight relationships.
  • High interlayer correlation diminishes community overlap enhancement in multilayered networks.
  • The geographic multilayer WSN model successfully enhances community overlap and preserves the Granovetterian structure.

Conclusions:

  • Societal structure is multilayered due to diverse interaction contexts.
  • Geographic constraints offer a mechanism to reconcile strong community ties with increased overlap in multilayered networks.
  • The proposed geographic multilayer WSN model provides a realistic framework for understanding complex social structures.