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Identifying a set of influential spreaders in complex networks.

Jian-Xiong Zhang1,2, Duan-Bing Chen1,2, Qiang Dong1,2

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Summary

VoteRank identifies decentralized spreaders for efficient information dissemination in complex networks. This method improves spreading speed and scale while being computationally efficient.

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Area of Science:

  • Network Science
  • Information Spreading Dynamics
  • Computational Social Science

Background:

  • Identifying influential spreaders is key for effective information dissemination in complex networks.
  • Traditional methods like PageRank and k-shell decomposition can select overlapping spreaders or be time-consuming.
  • Heuristic approaches also face limitations in selecting optimal, decentralized spreaders.

Purpose of the Study:

  • To propose a novel, efficient iterative method named VoteRank for identifying decentralized influential spreaders.
  • To evaluate VoteRank's effectiveness in enhancing information spreading ability and computational efficiency.

Main Methods:

  • VoteRank employs an iterative voting mechanism where nodes elect spreaders.
  • The voting ability of neighbors of elected spreaders is reduced in subsequent turns.
  • The method was tested on four real-world networks using Susceptible-Infected-Recovered (SIR) and Susceptible-Infected (SI) models.

Main Results:

  • VoteRank successfully identified decentralized spreaders with superior spreading ability compared to benchmark methods.
  • The method demonstrated significant improvements in both spreading rate and final affected scale.
  • VoteRank exhibited superior computational efficiency.

Conclusions:

  • VoteRank is an effective and efficient method for identifying decentralized influential spreaders in complex networks.
  • The proposed approach offers a promising alternative to traditional ranking and heuristic methods for information dissemination strategies.