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A Memetic Algorithm for Solving the Robust Influence Maximization Problem on Complex Networks against Structural

Delin Huang1, Xiaojun Tan1, Nanjie Chen1

  • 1School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen 518107, China.

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Summary
This summary is machine-generated.

This study introduces robust influence maximization (RIM) to select seed nodes in networks facing structural failures. A new algorithm, RIMMA, effectively identifies robust seeds for better information diffusion.

Keywords:
complex networksinfluence maximizationmemetic algorithmoptimizationrobustness

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Area of Science:

  • Network Science
  • Computer Science
  • Operations Research

Background:

  • Real-world transport systems are often modeled as networked systems requiring information or control signal dissemination.
  • The influence maximization problem focuses on selecting seed nodes to maximize spread, but existing methods often ignore network structural failures.
  • Robustness of information diffusion against network perturbations is critical for real-world applications.

Purpose of the Study:

  • To address the limitations of existing influence maximization strategies under network structural failures.
  • To develop a numerical performance criterion for seed selection in the presence of structural failures.
  • To introduce and evaluate a novel algorithm for robust influence maximization.

Main Methods:

  • Defined the robust influence maximization (RIM) problem to quantify seed performance under structural failures.
  • Developed a memetic optimization algorithm (MA), termed RIMMA, incorporating problem-specific operators for enhanced search capabilities.
  • Conducted experiments on both synthetic and real-world networks to validate the algorithm's effectiveness.

Main Results:

  • The proposed RIM problem provides a numerical criterion for evaluating seed robustness.
  • RIMMA demonstrates effectiveness in addressing the RIM problem.
  • Experimental results show RIMMA outperforms existing approaches in robust influence maximization.

Conclusions:

  • Existing influence maximization strategies are insufficient when networks experience structural failures.
  • The RIM problem and the RIMMA algorithm offer a robust solution for seed selection in dynamic networks.
  • RIMMA provides a superior approach for ensuring widespread information diffusion despite network perturbations.