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Influential Nodes Identification in Complex Networks via Information Entropy.

Chungu Guo1, Liangwei Yang1, Xiao Chen2

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Summary
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The EnRenew algorithm identifies influential nodes in complex networks using information entropy. This robust method improves node selection for applications like rumor control and epidemic prevention.

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Area of Science:

  • Complex networks analysis
  • Information theory applications

Background:

  • Identifying influential nodes is critical for applications like market advertising and rumor control.
  • Existing algorithms lack robustness and practicality for real-world complex network challenges.

Purpose of the Study:

  • To propose a robust and practical algorithm, EnRenew, for identifying influential nodes in complex networks.
  • To leverage information entropy for a more effective node selection strategy.

Main Methods:

  • Calculating initial spreading ability using node information entropy.
  • Dynamically updating spreading abilities of reachable nodes with an attenuation factor.
  • Iteratively selecting nodes with the highest information entropy.

Main Results:

  • EnRenew demonstrated significant performance improvements over state-of-the-art methods.
  • The algorithm achieved performance gains of 2.5% to 30.0% across various networks (CEnew, Email, Hamster, Router, Condmat, Amazon).
  • Simulations under the Susceptible-Infected-Recovered (SIR) model validated the algorithm's effectiveness.

Conclusions:

  • The EnRenew algorithm offers a novel and effective approach to influential node identification in complex networks.
  • Information entropy combined with a dynamic update strategy provides a powerful method for node mining.
  • The findings have implications for information spreading, epidemic prevention, and network analysis.