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Updated: Jul 29, 2025

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Influential nodes identification method based on adaptive adjustment of voting ability.

Guan Wang1,2, Syazwina Binti Alias2, Zejun Sun1

  • 1School of Information Engineering, Pingdingshan University, Pingdingshan, Henan, China.

Heliyon
|May 22, 2023
PubMed
Summary
This summary is machine-generated.

A novel algorithm for identifying influential nodes, Adaptive Voting Ability (AAVA), offers high accuracy and effectiveness. This method dynamically adjusts voting power, outperforming existing algorithms in complex network analysis.

Keywords:
Adaptive adjustmentComplex networkInfluential nodeVoting ability

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Area of Science:

  • Complex Networks
  • Network Science
  • Computational Social Science

Background:

  • Identifying influential nodes is crucial for applications like logistics, information dissemination, and disease spread.
  • Existing algorithms often struggle with accuracy, discrimination, and practical application in real-world networks.

Purpose of the Study:

  • To develop a novel, accurate, and easily executable algorithm for influential node identification.
  • To address limitations of current methods by considering local node attributes and neighbor contributions.

Main Methods:

  • Introduced the Adaptive Voting Ability (AAVA) algorithm, a voting-based approach.
  • AAVA dynamically adjusts a node's voting ability based on similarity with its neighbors, eliminating parameter tuning.
  • Evaluated AAVA against 13 other algorithms on 10 diverse networks using the SIR model for comparison.

Main Results:

  • AAVA demonstrated high consistency with the SIR model in identifying top influential nodes and through Kendall correlation.
  • The algorithm showed superior performance in simulating network infection spread.
  • Experimental results confirmed AAVA's accuracy and effectiveness across various network types and sizes.

Conclusions:

  • The AAVA algorithm is a highly accurate and effective method for identifying influential nodes in complex networks.
  • Its adaptive, parameter-free nature makes it suitable for real-world applications.
  • AAVA offers a significant improvement over existing influential node identification techniques.