Identifying a set of influential spreaders in complex networks.

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.