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Detecting Overlapping Communities in Modularity Optimization by Reweighting Vertices.

Chen-Kun Tsung1, Hann-Jang Ho2, Chien-Yu Chen3

  • 1Department of Computer Science and Information Engineering, National Chin-Yi University of Technology, Taichung 41170, Taiwan.

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|December 8, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method for detecting overlapping communities in networks by reweighting nodes. The genetic algorithm effectively identifies complex community structures often missed by traditional approaches.

Keywords:
community detectiondata miningmodularityoverlapping communities

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Area of Science:

  • Network Science
  • Computer Science
  • Data Mining

Background:

  • Traditional community detection algorithms struggle with overlapping community structures.
  • Modularity maximization and fuzzy modularity functions are insufficient for identifying complex, overlapping communities.

Purpose of the Study:

  • To address the challenge of detecting overlapping communities in real-world networks.
  • To propose a novel algorithm that accurately identifies nodes belonging to multiple communities.

Main Methods:

  • Formulated the overlapping community detection problem as a node weight allocation problem.
  • Developed an extended modularity measure based on reweighting nodes.
  • Utilized a genetic algorithm to solve the node weight allocation and detect overlapping communities.
  • Implemented three refinement strategies to enhance solution quality.

Main Results:

  • The proposed method successfully detected nontrivial overlapping nodes in both synthetic and real-world networks.
  • The algorithm identified valuable overlapping nodes that were overlooked by existing methods.
  • Experimental results demonstrate the effectiveness of the reweighting approach for overlapping community detection.

Conclusions:

  • The proposed node weight allocation and genetic algorithm approach offers a significant improvement for detecting overlapping communities.
  • This method enhances the accuracy and comprehensiveness of community detection in complex networks.
  • The findings suggest a more robust way to analyze network structures with overlapping community memberships.