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Clustering assessment in weighted networks.

Argimiro Arratia1, Martí Renedo Mirambell1

  • 1Department of Computer Sciences, Polytechnical University of Catalonia, Barcelona, Catalonia, Spain.

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Summary

This study introduces novel methods to validate community detection in weighted networks, even when community structures are unknown. The approach ensures reliable assessment of clustering algorithms for network analysis.

Keywords:
BootstrapClusteringMutual informationRRandomized graphSignificanceStabilityStochastic block modelWeighted networks

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Area of Science:

  • Network Science
  • Data Mining
  • Computational Statistics

Background:

  • Community detection in weighted networks is challenging, especially when ground truth is absent.
  • Existing validation methods often fail to account for network weights or unknown structures.
  • Assessing the significance and stability of identified clusters is crucial for reliable analysis.

Purpose of the Study:

  • To develop a systematic and robust approach for validating clustering results on weighted networks.
  • To introduce criteria for assessing the significance and stability of community structures.
  • To provide tools for evaluating clustering algorithms in scenarios with unknown community structures.

Main Methods:

  • Developed community scoring functions tailored for weighted networks and compared them against a novel switching model null hypothesis.
  • Introduced a non-parametric bootstrap method combined with information theory and combinatorial similarity metrics for cluster stability assessment.
  • Validated methods on synthetic weighted networks with known community structures (stochastic block model) and real-world datasets.

Main Results:

  • The proposed validation methods accurately identified superior clustering algorithms on synthetic and real-world weighted networks.
  • Demonstrated the effectiveness of significance testing using adapted scoring functions and a switching model.
  • Showcased the reliability of stability testing through bootstrap resampling and advanced similarity metrics.

Conclusions:

  • The developed validation framework provides a reliable means to assess clustering quality in weighted networks, particularly when community structures are initially unknown.
  • The methods are effective in distinguishing between high-performing and low-performing clustering algorithms.
  • The R package 'clustAnalytics' will offer these comprehensive validation tools for the research community.