Identification of nodes influence based on global structure model in complex networks.

Area of Science:

• Network Science
• Computational Social Science

Background:

• Identifying influential nodes in large-scale, dynamic complex networks is a significant challenge.
• Existing methods often focus on limited aspects, neglecting the comprehensive influence of a node.

Purpose of the Study:

• To propose a novel Global Structure Model (GSM) for accurate influential node identification.
• To evaluate GSM's effectiveness by considering both self-influence and global network structure.

Main Methods:

• Developed the Global Structure Model (GSM) incorporating self and global influence.
• Utilized the Susceptible Infected Recovered (SIR) model for efficiency evaluation.
• Compared GSM against nine standard algorithms on real-world and synthetic networks.

Main Results:

• GSM demonstrated superior performance in identifying influential nodes compared to baseline algorithms.
• The model effectively captures the complex interplay of node influence within diverse network topologies.

Conclusions:

• The Global Structure Model (GSM) offers a robust and effective approach for influential node identification.
• GSM's ability to consider global network structure enhances its applicability in various real-world scenarios.