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Betweenness centrality correlation in social networks.

K-I Goh1, E Oh, B Kahng

  • 1School of Physics and Center for Theoretical Physics, Seoul National University, Seoul 151-747, Korea.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|March 15, 2003
PubMed
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This study explores betweenness centrality (BC) correlations in scale-free (SF) networks. Assortative networks, common in social systems, show unique BC-BC correlations, suggesting similar influential environments regardless of an individual

Area of Science:

  • Network Science
  • Complex Systems Analysis
  • Social Network Analysis

Background:

  • Scale-free (SF) networks are characterized by a power-law degree distribution.
  • SF networks are classified as assortative, dissortative, or neutral based on degree-degree correlation.
  • Understanding network topology is crucial for analyzing information flow and influence.

Purpose of the Study:

  • To investigate the betweenness centrality (BC) correlation patterns across different types of SF networks.
  • To compare BC-BC correlations with degree-degree correlations in assortative, dissortative, and neutral networks.
  • To elucidate the structural properties of influential nodes within social networks.

Main Methods:

  • Analysis of betweenness centrality (BC) and its correlation with node degree.

Related Experiment Videos

  • Categorization of SF networks into assortative, dissortative, and neutral types.
  • Statistical examination of BC-BC correlation coefficients for each network type.

    • Main Results:

    • BC-BC correlation coefficients mirror degree-degree correlations in dissortative and neutral SF networks.
    • Assortative SF networks, prevalent in social systems, exhibit nontrivial BC-BC correlations.
    • The mean betweenness centrality of a node's neighbors is largely independent of the node's own BC.

    Conclusions:

    • The structural organization of influence differs significantly between assortative and other SF network types.
    • In assortative networks, individuals are embedded in environments with consistent levels of influence.
    • Findings offer insights into the propagation of information and influence in social structures.