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Network bipartivity.

Petter Holme1, Fredrik Liljeros, Christofer R Edling

  • 1Department of Physics, Umeå University, 901 87 Umeå, Sweden. holme@tp.umu.se

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|December 20, 2003
PubMed
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This study introduces two measures for network bipartivity, finding they accurately reflect network structure. Empirical social networks show distinct bipartivity values for romantic versus professional interactions.

Area of Science:

  • Network Science
  • Sociology
  • Computational Social Science

Background:

  • Agent-based systems with preferences for heterophilous interaction can form networks with varying degrees of bipartivity.
  • Quantifying network bipartivity is crucial for understanding social structures and interaction patterns.

Purpose of the Study:

  • To propose and evaluate two novel measures for quantifying network bipartivity.
  • To assess the relevance of these measures on model networks and empirical social data.

Main Methods:

  • Development of two bipartivity measures: one computationally intractable, one less complex.
  • Testing measures on model networks interpolating between bipartite graphs and graphs with odd circuits.
  • Analysis of empirical social networks from online interactions, professional collaborations, and surveys.

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Main Results:

  • Bipartivity measures increase with intuitive increases in network bipartivity on model systems.
  • Empirical social networks exhibit distinct bipartivity: high for romantic interactions, low for professional collaborations.
  • Low average network degree can impact the discriminative power of bipartivity measures.

Conclusions:

  • The proposed bipartivity measures are relevant and effective in characterizing network structures.
  • Network bipartivity offers insights into the nature of social interactions, distinguishing between relationship types.
  • Further research may refine measures for networks with limited connectivity.