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Identifying influential spreaders by gravity model.

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We developed a gravity model to identify key spreaders in complex networks. This model, including a local version, effectively predicts spreading dynamics for diseases and information, outperforming existing methods.

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Area of Science:

  • Network Science
  • Complex Systems
  • Epidemiology

Background:

  • Identifying influential nodes is critical for managing the spread of phenomena like diseases and information in complex networks.
  • Existing methods for identifying spreaders often face limitations in accuracy or computational efficiency.

Purpose of the Study:

  • To propose a novel gravity model for measuring node importance in spreading dynamics.
  • To introduce a computationally efficient local version of the gravity model.
  • To evaluate the performance of these models against state-of-the-art methods.

Main Methods:

  • Developed a gravity model incorporating neighborhood and path information to assess node influence.
  • Introduced a local gravity model with a truncation radius to reduce computational complexity and errors.
  • Conducted empirical analyses using Susceptible-Infected-Recovered (SIR) dynamics on fourteen real-world networks.

Main Results:

  • The proposed gravity model and its local version demonstrated competitive performance compared to existing methods.
  • The local gravity model effectively identified influential spreaders in various network structures.
  • An approximately linear relationship was observed between the optimal truncation radius and network average distance for the local model.

Conclusions:

  • The gravity model offers a robust approach for identifying influential spreaders in complex networks.
  • The local gravity model provides a computationally efficient alternative without significant loss of performance.
  • Findings suggest potential for optimizing the local gravity model based on network characteristics.