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Influence maximization: Divide and conquer.

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Summary
This summary is machine-generated.

This study introduces a novel framework to improve influence maximization by dividing networks into sectors. This approach enhances influencer identification, especially in complex, large-scale networks.

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Area of Science:

  • Network Science
  • Computer Science
  • Data Mining

Background:

  • Influence maximization is crucial for applications like viral marketing and information diffusion.
  • Existing heuristic metrics for identifying influencers have limitations in performance.
  • Large-scale networks pose significant challenges for traditional influence maximization methods.

Purpose of the Study:

  • To introduce a novel framework to enhance the performance of influence maximization metrics.
  • To explore efficient methods for dividing networks into sectors for improved analysis.
  • To validate the framework's effectiveness across diverse network types.

Main Methods:

  • Developing a framework that divides networks into sectors of influence.
  • Employing graph partitioning, hyperbolic embedding, and community structure for sector identification.
  • Systematic analysis and validation on real and synthetic network datasets.

Main Results:

  • The proposed framework significantly boosts the performance of influence maximization metrics.
  • Performance gains increase with network modularity and heterogeneity.
  • Sector division is computationally efficient, scaling linearly with network size.

Conclusions:

  • Dividing networks into sectors is an effective strategy for improving influence maximization.
  • The framework is scalable and applicable to large-scale influence maximization problems.
  • This approach offers a practical solution for identifying key influencers in complex networks.