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This study introduces a new algorithm to identify critical nodes in complex networks by combining global and local information. This approach improves accuracy and efficiency in network analysis and control.

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Area of Science:

  • Network Science
  • Computational Science

Background:

  • Identifying key nodes is crucial for managing complex networks.
  • Existing methods often rely on either local or global information, leading to limitations.

Purpose of the Study:

  • To propose an accurate and efficient algorithm for critical node mining in complex networks.
  • To overcome the shortcomings of methods that consider only local or global information.

Main Methods:

  • The proposed algorithm integrates global and local influences for node identification.
  • Global influence is assessed using K-shell hierarchical information.
  • Local influence considers node degree, adjacent node values (derived from degree and K-shell info), and neighbor similarity.

Main Results:

  • The algorithm effectively identifies influential nodes by leveraging both global and local network properties.
  • It addresses issues of high complexity found in global-only methods and low accuracy in local-only methods.
  • Simulations on SIR and SI models and twelve real-world networks validate its performance.

Conclusions:

  • The developed Global and Local Information (GLI) algorithm offers a superior approach to critical node identification.
  • It provides a balanced consideration of network topology for enhanced analysis and control.