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Related Experiment Videos

Social inertia in collaboration networks.

José J Ramasco1, Steven A Morris

  • 1Physics Department, Emory University, Atlanta, Georgia 30322, USA. jose.ramasco@emory.edu

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|February 21, 2006
PubMed
Summary

This study introduces social inertia to analyze author collaboration networks. Higher social inertia indicates a stronger tendency for researchers to maintain existing partnerships, impacting network dynamics.

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Area of Science:

  • Bibliometrics
  • Network Science
  • Sociology of Science

Background:

  • Collaboration networks are crucial in scientific advancement.
  • Understanding the dynamics of these networks is key to fostering innovation.
  • Existing models often simplify the persistence of scientific collaborations.

Purpose of the Study:

  • To introduce and quantify 'social inertia' in scientific collaboration networks.
  • To analyze the properties and distribution of social inertia.
  • To investigate the relationship between social inertia and other network characteristics.

Main Methods:

  • Utilizing weighted graphs to represent collaboration networks, where edge weight signifies repeated partnerships.
  • Defining and calculating social inertia based on the frequency of author collaborations.
  • Analyzing empirical datasets to examine social inertia's probability distribution and correlations.
  • Comparing empirical findings with predictions from a theoretical network growth model.

Main Results:

  • Social inertia, the tendency to maintain collaborations, can be effectively quantified.
  • Empirical analysis reveals specific probability distributions and correlations for social inertia.
  • The study identifies how social inertia relates to other network properties and neighboring interactions.
  • Observed social inertia patterns are contrasted with theoretical model predictions.

Conclusions:

  • Social inertia is a significant factor influencing the structure and evolution of collaboration networks.
  • The proposed weighted graph approach provides a robust framework for studying collaboration persistence.
  • Findings offer insights into the stability and growth patterns of scientific communities.

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