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Influential nodes identification using network local structural properties.

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A new algorithm, LENC, identifies influential nodes in complex networks by considering edge weights and information entropy. This method improves prediction and control of disease and rumor spread, outperforming existing approaches.

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Area of Science:

  • Network Science
  • Information Theory
  • Computational Social Science

Background:

  • Complex networks are growing, complicating disease and rumor control.
  • Accurate identification of influential nodes is crucial for network system prediction and control.
  • Existing algorithms often neglect edge impact or suffer from high computational complexity.

Purpose of the Study:

  • To propose a novel algorithm for influential node evaluation in complex networks.
  • To address limitations of existing methods, including edge impact and computational cost.
  • To enhance the accuracy and efficiency of influential node detection.

Main Methods:

  • Developed the LENC (Local Edge-weighted Network Centrality) algorithm based on information entropy theory.
  • Incorporated edge weight distribution and influence on neighbor nodes into the evaluation.
  • Validated the algorithm on eight real-world networks.

Main Results:

  • The LENC algorithm demonstrated effectiveness and accuracy in identifying influential nodes.
  • Infection size simulations using the SIR model confirmed the algorithm's performance.
  • Kendall's tau coefficient verified the consistency of LENC rankings with SIR model outcomes.

Conclusions:

  • The proposed LENC algorithm provides a more accurate and efficient method for influential node detection.
  • Considering edge weights and information entropy significantly improves network analysis.
  • LENC offers a promising tool for managing and controlling spread dynamics in complex networks.