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Homophily, the tendency to connect with similar others, is now modeled across all network scales. This new framework reveals how group-specific homophily affects network dynamics like epidemic spread and information diffusion.

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Area of Science:

  • Network Science
  • Sociology
  • Computational Social Science

Background:

  • Homophily significantly influences network structure and function.
  • Current models oversimplify homophily by considering only one scale, neglecting its multi-scale nature.

Purpose of the Study:

  • To introduce a novel maximum-entropy random graph model for capturing homophily across all social scales.
  • To decompose homophily into within- and across-group contributions.
  • To analyze the impact of group-dependent homophily on network dynamics.

Main Methods:

  • Developed a maximum-entropy random graph model.
  • Incorporated parameters for group-specific homophily at various scales.
  • Decomposed homophily into within- and across-group components.
  • Recovered the stochastic block model as a special case.

Main Results:

  • The model captures homophily across all network scales, unlike previous approaches.
  • It decomposes homophily into within- and across-group contributions.
  • Group-dependent homophily significantly alters network percolation thresholds.

Conclusions:

  • Network homophily varies substantially across different group sizes and scales.
  • This heterogeneity critically impacts predictions for epidemic spread, information diffusion, and intervention effectiveness.
  • Ignoring multi-scale homophily leads to misjudgments of complex system connectivity and dynamics.