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A semi-synchronous label propagation algorithm with constraints for community detection in complex networks.

Jia Hou Chin1, Kuru Ratnavelu1

  • 1Institute of Mathematical Science, University of Malaya, Kuala Lumpur, Malaysia.

Scientific Reports
|April 5, 2017
PubMed
Summary

This study introduces an improved community detection algorithm, enhancing the label propagation algorithm (LPA) to overcome randomness and trivial detection issues in complex networks. The new method demonstrates superior performance in identifying network communities.

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Area of Science:

  • Complex Networks
  • Network Science
  • Data Mining

Background:

  • Community structure is crucial for understanding complex networks.
  • Label Propagation Algorithm (LPA) is efficient but suffers from randomness.
  • Existing modifications like CLPA-GNR still struggle with weak community structures and trivial detection.

Purpose of the Study:

  • To propose an improved community detection algorithm based on CLPA-GNR.
  • To address the limitations of randomness and trivial detection in LPA.
  • To enhance the accuracy and quality of community detection in complex networks.

Main Methods:

  • A novel algorithm improves CLPA-GNR by updating nodes synchronously and asynchronously.
  • Utilizes the Sørensen-Dice index for initial community detection and tie-breaking.

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  • Incorporates constraints during label propagation and community merging.

    • Main Results:

    • The proposed algorithm effectively avoids trivial detection.
    • Demonstrates substantial improvements in the quality of community detection.
    • Performance validated on benchmark and real-world network datasets.

    Conclusions:

    • The improved algorithm offers a robust solution for community detection in complex networks.
    • It overcomes key limitations of previous LPA-based methods.
    • Provides higher accuracy and reliability for network analysis.