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Identifying Key Nodes for the Influence Spread Using a Machine Learning Approach.

Mateusz Stolarski1, Adam Piróg2, Piotr Bródka1

  • 1Department of Artificial Intelligence, Wrocław University of Science and Technology, 50-370 Wrocław, Poland.

Entropy (Basel, Switzerland)
|November 27, 2024
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Summary
This summary is machine-generated.

This study introduces "Smart Bins" to improve machine learning for identifying key influencers in complex networks. The enhanced framework accurately predicts influence spread and generalizes across various network types.

Keywords:
influence spreadnode classificationsocial networksunsupervised learning

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Area of Science:

  • Network Science
  • Machine Learning
  • Complex Systems Analysis

Background:

  • Identifying key nodes is crucial for applications like viral marketing and epidemic control.
  • Machine learning (ML) methods show promise but require refinement for accuracy and generalization.

Purpose of the Study:

  • To develop an enhanced ML framework for identifying key nodes in complex networks, specifically for the Independent Cascade model.
  • To address challenges in obtaining training labels and improving model generalization.

Main Methods:

  • Introduction of a novel "Smart Bins" technique for improved label generation in ML training.
  • Development of an ML-based framework to predict influence spread and node characteristics.

Main Results:

  • "Smart Bins" demonstrate superiority over existing methods for generating training labels.
  • The proposed framework accurately predicts node influence and reveals additional spreading process characteristics.
  • Extensive testing confirms the framework's robust generalization across diverse network structures and sizes.

Conclusions:

  • The enhanced ML framework offers a significant advancement in identifying key nodes for influence spread prediction.
  • The "Smart Bins" method and the framework's ability to extract further spreading characteristics represent novel contributions to network science.