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Quantifying edge significance on maintaining global connectivity.

Yuhua Qian1,2, Yebin Li2, Min Zhang3

  • 1Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Shanxi University, Taiyuan, 030006, China.

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Summary
This summary is machine-generated.

Identifying critical network edges is vital for system robustness. Our link entropy (LE) approach effectively quantifies edge significance for global connectivity, outperforming existing methods.

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Area of Science:

  • Network science
  • Graph theory
  • Information theory

Background:

  • Global network connectivity is crucial for system stability.
  • Failure of key network edges can cause catastrophic system collapse.
  • Understanding edge significance enhances network robustness analysis.

Purpose of the Study:

  • To introduce a novel metric, link entropy (LE), for quantifying edge significance in maintaining global network connectivity.
  • To compare the effectiveness of LE against six established edge significance indices.
  • To evaluate the performance of LE in identifying critical network edges.

Main Methods:

  • The study proposes the link entropy (LE) approach based on network topological information.
  • LE is compared with edge betweenness centrality, degree product, bridgeness, diffusion importance, topological overlap, and k-path edge centrality.
  • Experimental validation is performed to assess the performance of LE.

Main Results:

  • The proposed link entropy (LE) approach demonstrates superior performance in quantifying edge significance.
  • LE effectively identifies edges crucial for maintaining global network connectivity.
  • Experimental results confirm the outperformance of LE compared to other indices.

Conclusions:

  • Link entropy (LE) is a robust and effective metric for assessing edge significance in networks.
  • The LE approach provides valuable insights into network robustness and failure analysis.
  • This method aids in designing more resilient and reliable network systems.