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An improved two-stage label propagation algorithm based on LeaderRank.

Miaomiao Liu1,2, Jinyun Yang1, Jingfeng Guo3

  • 1School of Computer and Information Technology, Northeast Petroleum University, Daqing, Heilongjiang, China.

Peerj. Computer Science
|September 12, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces an improved two-stage label propagation algorithm using LeaderRank to enhance community detection stability and modularity. The new method offers more accurate and stable network community partitioning results.

Keywords:
Community divisionLabel propagationLeaderRankModularityNode influenceWeak community

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Area of Science:

  • Network science
  • Graph theory
  • Data mining

Background:

  • Traditional label propagation algorithms suffer from instability and low modularity due to random node selection and label updates.
  • Existing methods struggle with label oscillation and accurately identifying initial community structures.

Purpose of the Study:

  • To propose an improved two-stage label propagation algorithm based on LeaderRank to address the limitations of traditional methods.
  • To enhance the stability, modularity, and accuracy of community detection in complex networks.

Main Methods:

  • A two-stage approach was developed: the first stage uses participation coefficient (PC) for node updating order and a new similarity measure for label selection.
  • The second stage treats initial communities as nodes, using PC for merging sequences and optimizing for modularity.
  • Node influence and community connectivity were utilized for initial structure and merging decisions.

Main Results:

  • The improved algorithm demonstrated superior performance in community partitioning compared to dozens of classic algorithms.
  • Experimental results on diverse realistic and artificial networks showed increased stability and accuracy in community detection.
  • The algorithm performed well on large-scale datasets (up to 50,000 nodes), validating its computational efficiency.

Conclusions:

  • The proposed LeaderRank-based two-stage label propagation algorithm significantly improves community detection stability and modularity.
  • This enhanced method provides more accurate and reliable community structures, especially for large and complex networks.
  • The algorithm offers a robust and efficient solution for community detection in network science.