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Measuring group fairness in community detection.

Elze de Vink1, Frank W Takes1, Akrati Saxena1

  • 1Leiden Institute of Advanced Computer Science, Leiden University, Leiden, The Netherlands.

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Summary
This summary is machine-generated.

This study introduces new group fairness metrics for community detection algorithms, addressing inequalities affecting minority groups in networks. Infomap and Significance methods show strong performance and fairness across various networks.

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Area of Science:

  • Network analysis
  • Algorithmic fairness
  • Sociotechnical systems

Background:

  • Community structures are fundamental to network analysis and are shaped by social factors like ethnicity and gender.
  • Real-world networks exhibit structural inequalities, with majority and minority groups.
  • Traditional community detection algorithms may produce unfair outcomes for underrepresented groups.

Purpose of the Study:

  • To propose novel group fairness metrics for evaluating community detection methods.
  • To conduct a comparative analysis of common community detection algorithms regarding performance and fairness.
  • To investigate the trade-off between performance and fairness in community detection.

Main Methods:

  • Development of new group fairness metrics tailored for community detection.
  • Comparative evaluation of community detection algorithms on synthetic (LFR, ABCD, HICH-BA) and real-world networks.
  • Analysis of the performance-fairness trade-off across different algorithmic approaches.

Main Results:

  • The fairness-performance trade-off differs significantly among community detection methods.
  • No single method consistently optimizes both performance and fairness.
  • Infomap and Significance methods demonstrate high performance and fairness across diverse community types and networks.

Conclusions:

  • Existing community detection methods exhibit varied fairness-performance characteristics.
  • The proposed metrics offer a framework for assessing and improving algorithmic fairness.
  • Insights guide the design of more equitable and effective community detection algorithms for real-world networks.