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Dismantling network by removing weak vertices.

Ming-Wei Wu1, Xing-Qin Qi1, Zhu-Lou Cao1

  • 1School of Mathematics and Statistics, Shandong University, Weihai 264209, People's Republic of China.

Chaos (Woodbury, N.Y.)
|June 24, 2025
PubMed
Summary
This summary is machine-generated.

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This study introduces a novel spectral method to identify and remove weak vertices in networks, improving network dismantling. The new approach effectively reduces network connectivity by targeting less robust nodes.

Area of Science:

  • Graph Theory
  • Network Science
  • Computer Science

Background:

  • Network dismantling aims to reduce network connectivity by removing vertices.
  • Spectral methods are common but often overlook weakly connected vertices.
  • Identifying and removing these weak vertices is crucial for robust network analysis.

Purpose of the Study:

  • To propose a new spectral-based method for network dismantling.
  • To identify and remove weak vertices that are often ignored by existing methods.
  • To improve the efficiency and effectiveness of network dismantling.

Main Methods:

  • Utilized the second smallest eigenvector of the Laplacian matrix to detect weak vertices.
  • Employed a greedy removal strategy for identified weak vertices.

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  • Iteratively removed vertices until the largest connected component reached a target size.

    • Main Results:

    • The proposed method successfully identifies communities and a distinct set of weak vertices.
    • Experimental results on real networks show superior performance compared to state-of-the-art methods.
    • The method effectively decreases network connectivity by targeting robustly weak nodes.

    Conclusions:

    • The novel spectral method offers an effective approach to network dismantling.
    • Targeting weak vertices is a promising strategy for network analysis and security.
    • The proposed technique outperforms existing methods in reducing network connectivity.