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Updated: May 16, 2025

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Influential nodes identification for complex networks based on multi-feature fusion.

Shaobao Li1, Yiran Quan1, Xiaoyuan Luo1

  • 1School of Electrical Engineering, Yanshan University, Qinhuangdao, 066004, China.

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|April 3, 2025
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Summary

Identifying critical nodes in complex networks is improved by the new Degree-k-shell-Betweenness Centrality (DKBC) model. This model uniquely integrates spatial information, enhancing accuracy for key node identification in network analysis.

Keywords:
k-shell algorithmBetweenness centralityComplex networksGravity modelInfluential node identification

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Area of Science:

  • Network Science
  • Graph Theory
  • Computational Social Science

Background:

  • Identifying critical nodes is crucial for understanding complex networks.
  • Existing methods often neglect spatial information, limiting accuracy.
  • There is a need for advanced centrality models that incorporate spatial attributes.

Purpose of the Study:

  • To introduce an advanced centrality model, Degree-k-shell-Betweenness Centrality (DKBC), for accurate key node identification.
  • To integrate node degree, spatial positioning, and intermediate degree into a unified centrality measure.
  • To demonstrate the superiority of the DKBC model over traditional methods.

Main Methods:

  • Developed the Degree-k-shell-Betweenness Centrality (DKBC) model based on the gravity principle.
  • Integrated node degree, spatial information, and betweenness centrality.
  • Validated diffusion capacity using Susceptible-Infected-Recovered (SIR) and Independent Cascade (IC) models.
  • Assessed correlation using the Kendall coefficient τ.

Main Results:

  • The DKBC model significantly improves the accuracy of key node identification.
  • Empirical validation on twelve real-world networks confirmed the model's effectiveness.
  • Comparative analysis showed superior performance against benchmark algorithms.
  • The model demonstrated enhanced diffusion capacity in network simulations.

Conclusions:

  • Incorporating spatial information into centrality measures is vital for accurate key node identification.
  • The DKBC model offers a more effective approach for network analysis and practical applications.
  • This research advances the field of complex network analysis by providing a novel, spatially-aware centrality measure.