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Ranking influential nodes in complex networks with community structure.

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This study introduces a community-aware ranking strategy to identify influential spreaders in networks. It outperforms existing methods by selecting distant nodes, improving strategies for phenomena like disease diffusion.

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Area of Science:

  • Network Science
  • Computational Social Science
  • Epidemiology

Background:

  • Identifying influential nodes is crucial for managing spreading phenomena.
  • Traditional centrality measures often identify nearby nodes, limiting their influence zones.
  • Real-world networks exhibit community structures that are often overlooked.

Purpose of the Study:

  • To propose a novel community-aware ranking strategy for node importance quantification.
  • To enhance the selection of influential spreaders by considering network community structure.
  • To evaluate the strategy's effectiveness in controlling or accelerating network phenomena.

Main Methods:

  • Developed a ranking strategy that leverages community structure in networks.
  • Integrated the strategy with existing centrality measures.
  • Tested the strategy using real-world and synthetic networks within a Susceptible-Infected-Recovered (SIR) diffusion model.

Main Results:

  • The proposed community-aware ranking strategy significantly outperforms community-agnostic methods.
  • The strategy effectively identifies a set of distant, highly influential spreaders.
  • Performance is superior in networks with strong, heterogeneous community structures.

Conclusions:

  • Community structure is a vital feature for accurate node influence assessment.
  • The proposed strategy offers a more effective approach for targeting spreaders in complex networks.
  • This method has broad applicability in network analysis and intervention strategies.