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Leto Peel1,2, Jean-Charles Delvenne1,3, Renaud Lambiotte4

  • 1Institute of Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM), Université Catholique de Louvain, Louvain-la-Neuve B-1348, Belgium; renaud.lambiotte@maths.ox.ac.uk leto.peel@uclouvain.be jean-charles.delvenne@uclouvain.be.

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Summary
This summary is machine-generated.

This study introduces a new method to measure assortative mixing in networks at the node level, revealing diverse local patterns beyond global averages. This approach helps analyze complex network organization and understand heterogeneous mixing behaviors.

Keywords:
assortativitycomplex networksmultiscalenode metadata

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Area of Science:

  • Network Science
  • Sociology
  • Data Analysis

Background:

  • Assortative mixing describes the tendency of nodes with similar attributes to connect in networks.
  • Existing global measures like the assortativity coefficient may not capture heterogeneous mixing patterns in complex systems.
  • Understanding local mixing patterns is crucial for comprehending network organization and social structures.

Purpose of the Study:

  • To develop a localized method for quantifying assortativity at the node level.
  • To analyze the distribution of mixing patterns across multiple scales within networks.
  • To provide a more nuanced understanding of network organization beyond global averages.

Main Methods:

  • Introduction of a novel approach to localize the global measure of assortativity.
  • Application of the method to analyze mixing patterns at the node level across various scales.
  • Qualitative evaluation of the distribution of mixing patterns in real-world networks.

Main Results:

  • The developed method successfully localizes assortativity measurements.
  • Analysis reveals that assortativity distributions in many real-world networks are skewed, overdispersed, and multimodal.
  • The findings highlight significant variations in local mixing patterns within networks.

Conclusions:

  • The localized approach offers a clearer perspective on network mixing patterns.
  • This method enables a more detailed examination of heterogeneity in network structures.
  • Understanding localized assortativity is key to characterizing complex network organization.