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A Two-Phase Feature Selection Method for Identifying Influential Spreaders of Disease Epidemics in Complex Networks.

Xiya Wang1, Yuexing Han1,2, Bing Wang1

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Entropy (Basel, Switzerland)
|July 29, 2023
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Identifying influential spreaders in epidemics is crucial. This study introduces a two-phase feature selection method to efficiently pinpoint these key individuals, improving epidemic control strategies.

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feature selectioninfluential node identificationmachine learningnetwork epidemiology

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Area of Science:

  • Network epidemiology
  • Computational social science
  • Infectious disease modeling

Background:

  • Identifying influential spreaders is vital for understanding epidemic dynamics and control.
  • Traditional centrality measures have limitations in universality and computational efficiency for large networks.
  • Machine learning approaches improve spreader identification but can be computationally intensive.

Purpose of the Study:

  • To develop a computationally efficient, two-phase feature selection method for identifying influential spreaders.
  • To determine optimal feature combinations for spreader identification across different network types.
  • To reduce feature dimensionality while maintaining accuracy in identifying key individuals.

Main Methods:

  • A two-phase feature selection approach was employed.
  • Optimal feature combinations were identified based on network topology (Barabasi-Albert, Erdős-Rényi, Watts-Strogatz) and dataset imbalance.
  • The method was validated on both synthetic and real-world network data.

Main Results:

  • For Barabasi-Albert (BA) networks, a combination including two-hop neighborhood centrality is fundamental.
  • For Erdős-Rényi (ER) networks, degree centrality is essential.
  • Feature selection was found to be unnecessary for Watts-Strogatz (WS) networks, and selected features on real-world networks showed high similarity to synthetic network findings.

Conclusions:

  • The proposed two-phase feature selection method effectively reduces feature dimensions for identifying influential spreaders.
  • The optimal feature combinations are network-dependent, offering tailored strategies for different network structures.
  • This approach provides a novel and efficient pathway for identifying superspreaders to enhance epidemic control.