Identifying influential spreaders by gravity model considering multi-characteristics of nodes

Area of Science:

• Network Science
• Complex Systems
• Information Diffusion

Background:

• Identifying influential spreaders in complex networks is a significant challenge.
• Existing methods often rely on single node characteristics, proving inadequate.
• Multi-characteristic approaches are emerging to improve spreader identification accuracy.

Purpose of the Study:

• To propose a novel gravity model for identifying influential spreaders.
• To integrate multiple node characteristics for enhanced identification accuracy.
• To evaluate the model's performance against state-of-the-art methods.

Main Methods:

• Developed a gravity model incorporating node degree, neighbor influence, node location, and path information.
• Analyzed Susceptible-Infected-Recovered (SIR) spreading dynamics.
• Conducted empirical analyses on ten real-world complex networks.

Main Results:

• The proposed gravity model consistently outperformed existing methods in identifying influential spreaders.
• The model demonstrated strong performance even when considering only second-order neighborhoods.
• Empirical analyses confirmed the model's effectiveness across diverse network structures.

Conclusions:

• The proposed gravity model offers a superior approach to influential spreader identification in complex networks.
• Integrating multi-characteristic information significantly enhances the accuracy of spreader detection.
• The model's efficiency and competitive performance highlight its practical applicability.