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Post-Processing Partitions to Identify Domains of Modularity Optimization.

William H Weir1,2, Scott Emmons1, Ryan Gibson1

  • 1Carolina Center for Interdisciplinary Applied Mathematics, Department of Mathematics, University of North Carolina, Chapel Hill, NC 27599, USA.

Algorithms
|October 20, 2017
PubMed
Summary
This summary is machine-generated.

The Convex Hull of Admissible Modularity Partitions (CHAMP) algorithm efficiently prunes network community structures. CHAMP identifies optimal partitions across parameter domains, significantly reducing analysis complexity for robust community detection.

Keywords:
community detectionmodularitymultilayer networksnetworksresolution parameter

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Area of Science:

  • Network science
  • Computational complexity
  • Data mining

Background:

  • Community detection algorithms often yield numerous, sometimes redundant, network partitions.
  • Evaluating and selecting the most robust community structures from multiple heuristic outputs is challenging.
  • Parameter spaces in network analysis, especially for multilayer networks, increase the complexity of partition evaluation.

Purpose of the Study:

  • To introduce the Convex Hull of Admissible Modularity Partitions (CHAMP) algorithm.
  • To provide a method for pruning and prioritizing network community structures.
  • To enable robust selection of community structures by identifying optimal parameter domains.

Main Methods:

  • CHAMP algorithm identifies the modularity optimization domain for each partition.
  • Partitions with empty domains are discarded, yielding an "admissible" subset.
  • The method supports multi-dimensional parameter spaces, including resolution and interlayer coupling parameters.

Main Results:

  • CHAMP effectively prunes large sets of network partitions.
  • Demonstrated utility on example networks, reducing partition sets by 20-to-1785 times.
  • Identified admissible partitions that remain potentially optimal over specific parameter domains.

Conclusions:

  • CHAMP offers a computationally efficient approach to network community structure analysis.
  • The algorithm facilitates more appropriate selection of robust community structures.
  • CHAMP enhances the interpretability and usability of results from multiple community detection heuristics.