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Measuring the robustness of network community structure using assortativity.

Daizaburo Shizuka1, Damien R Farine2

  • 1School of Biological Sciences, University of Nebraska-Lincoln, NE, U.S.A.

Animal Behaviour
|March 8, 2016
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Summary

Researchers developed a new method to assess social network community structure robustness in animals. Community assortativity (r_com) provides confidence levels for network analysis, addressing sampling errors in animal social network data.

Keywords:
bootstrappingcommunity detectionflockgolden-crowned sparrowmodularitysocial networkthornbilltit

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Area of Science:

  • Social network analysis
  • Animal behavior
  • Ecology

Background:

  • Social networks exhibit community structure in many species.
  • Modularity (Q) measures community structure but assumes complete data.
  • Animal social network data often suffers from sampling biases and limited observations.

Purpose of the Study:

  • To develop a robust method for assessing community structure in animal social networks.
  • To address limitations of modularity (Q) due to sampling errors.
  • To introduce community assortativity (r_com) as a measure of confidence in community assignments.

Main Methods:

  • Extended existing bootstrapping frameworks for network metrics.
  • Developed and simulated community assortativity (r_com).
  • Applied modularity (Q) and community assortativity (r_com) to avian social network datasets.

Main Results:

  • Modularity (Q) reliably detects structured associations in simulated networks.
  • Community assortativity (r_com) accurately reflects confidence based on association detectability.
  • The new method was validated using real-world avian social network data.

Conclusions:

  • Community assortativity (r_com) offers a robust approach to analyzing animal social networks.
  • This method improves the rigor of analyses by accounting for sampling limitations.
  • Facilitates more reliable studies of population-level social structures across species.