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Identifying and quantifying potential super-spreaders in social networks.

Dayong Zhang1, Yang Wang2, Zhaoxin Zhang3

  • 1Department of New Media and Arts, Harbin Institute of Technology, Harbin, 150001, China.

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A new CumulativeRank algorithm accurately identifies influential spreaders in social networks by combining local and global node performance. This method offers improved accuracy and efficiency for large-scale network analysis.

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Area of Science:

  • Network Science
  • Computational Social Science

Background:

  • Identifying influential spreaders is crucial for social network analysis and information dissemination.
  • Existing methods often oversimplify node importance by focusing on single attributes, leading to accuracy and simplicity trade-offs.

Purpose of the Study:

  • To propose the CumulativeRank algorithm for accurately quantifying nodal spreading abilities.
  • To identify potential super-spreaders in social networks effectively.

Main Methods:

  • The CumulativeRank algorithm integrates local (direct and indirect neighborhood influence) and global (network tenacity) node performances.
  • Experiments utilized the Susceptible-Infected-Recovered (SIR) model on real-world social networks.

Main Results:

  • The CumulativeRank algorithm demonstrated high accuracy and stability in identifying influential spreaders.
  • Comparative analysis revealed lower time complexity compared to existing algorithms.

Conclusions:

  • CumulativeRank offers a balanced and accurate approach to measuring nodal spreading abilities.
  • The algorithm's efficiency makes it suitable for analyzing large-scale social networks.