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Improved Link Entropy with Dynamic Community Number Detection for Quantifying Significance of Edges in Complex Social

Vasily Lubashevskiy1, Seval Yurtcicek Ozaydin2, Fatih Ozaydin1,3

  • 1Institute for International Strategy, Tokyo International University, 1-13-1 Matoba-kita, Kawagoe 350-1197, Saitama, Japan.

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|February 25, 2023
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Summary

This study enhances edge significance quantification in complex networks by improving the Link Entropy method. Experiments show the proposed approach, using Louvain and Leiden algorithms, outperforms existing methods for community detection and edge analysis.

Keywords:
LeidenLouvainWalktrapdeep link entropyedge significancelink entropysocial networks

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Area of Science:

  • Network science
  • Data analysis
  • Computational social science

Background:

  • Community discovery is crucial for analyzing complex networks, including social and political systems.
  • Quantifying edge significance aids in understanding network structure and dynamics.

Purpose of the Study:

  • To propose an improved method for quantifying edge significance in complex networks.
  • To evaluate the performance of the enhanced Link Entropy method against existing approaches.

Main Methods:

  • The study introduces an improved Link Entropy method for edge significance quantification.
  • Community detection was performed using Louvain, Leiden, and Walktrap algorithms.
  • Experiments were conducted on various benchmark networks to assess performance.

Main Results:

  • The proposed method demonstrates superior performance in quantifying edge significance compared to the original Link Entropy method.
  • Louvain and Leiden algorithms were identified as optimal for community number detection in this context.
  • The study highlights the effectiveness of the enhanced method in analyzing network communities.

Conclusions:

  • The enhanced Link Entropy method provides a more accurate quantification of edge significance.
  • Leiden or Louvain algorithms are recommended for community number detection when quantifying edge significance.
  • Future work includes developing algorithms for community number discovery and membership uncertainty computation.