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Identifying influential spreaders in complex networks by an improved gravity model.

Zhe Li1, Xinyu Huang2

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Identifying influential spreaders in networks is crucial. This study introduces a novel index combining degree centrality and k-shell decomposition, improving node influence ranking in propagation dynamics.

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Area of Science:

  • Network Science
  • Complex Systems
  • Computational Physics

Background:

  • Identifying influential spreaders is a key challenge in network science, impacting various dynamic processes.
  • Existing methods like degree centrality and k-shell decomposition suffer from resolution limitations, hindering accurate influence assessment.
  • These limitations affect the efficiency and reliability of algorithms designed to rank node influence.

Purpose of the Study:

  • To address the resolution limitation problem in identifying influential spreaders.
  • To propose a novel high-resolution index by integrating degree centrality and k-shell decomposition.
  • To develop an improved gravity model for measuring node importance in propagation dynamics.

Main Methods:

  • Developed a new index by combining degree centrality and k-shell decomposition to overcome resolution limitations.
  • Proposed an enhanced gravity model incorporating the new index to quantify node importance.
  • Validated the model's performance through experiments on ten real-world networks.

Main Results:

  • The proposed high-resolution index effectively distinguishes node influence, mitigating the resolution limitation.
  • The improved gravity model demonstrated superior performance compared to state-of-the-art methods.
  • Quantitative improvements were observed in ranking performance (Kendall's rank correlation) and ranking efficiency (monotonicity).

Conclusions:

  • The novel index and improved gravity model offer a more accurate and efficient approach to identifying influential spreaders.
  • This work contributes to advancing network science by providing better tools for analyzing propagation dynamics.
  • The findings have implications for understanding and controlling information diffusion, disease spread, and other network-based phenomena.