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Finite-state parameter space maps for pruning partitions in modularity-based community detection.

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This study introduces a new framework to improve community detection in networks. It prunes partitions to find consistent results, addressing issues with heuristic algorithms and parameter selection in modularity-based methods.

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Area of Science:

  • Network Science
  • Data Mining
  • Computational Social Science

Background:

  • Community detection is crucial for network analysis, with modularity-based methods being widely used.
  • Current methods face challenges with parameter selection and heuristic algorithm stochasticity, leading to variable results.
  • Existing approaches often require manual tuning of resolution and coupling parameters.

Purpose of the Study:

  • To develop a unified framework for pruning network partitions to enhance community detection consistency.
  • To address the stochasticity inherent in heuristic optimization of modularity.
  • To provide guidance on parameter space selection for community detection.

Main Methods:

  • Combined recent developments into a simple-to-use framework for pruning partitions.
  • Utilized an equivalence with the objective function for inference of a degree-corrected planted partition stochastic block model (SBM).
  • Derived and validated upper bounds for the resolution parameter in constrained SBMs.

Main Results:

  • The pruning framework highlights a small subset of self-consistent partitions.
  • The method effectively reduces problems associated with the stochasticity of heuristic algorithms.
  • Derived resolution parameter bounds were demonstrated to hold in practice, guiding parameter selection.
  • Publicly available code facilitates practical application of the pruning procedure.

Conclusions:

  • The developed framework offers a novel baseline for practical community detection using modularity.
  • The approach mitigates issues related to parameter sensitivity and algorithmic stochasticity.
  • This work provides a more robust and reliable method for identifying network communities.