Hunting for vital nodes in complex networks using local information

Area of Science:

• Network Science
• Complex Systems Analysis
• Computational Social Science

Background:

• Real-world complex networks exhibit heterogeneous degree distributions, where vital nodes significantly impact network function and spreading phenomena.
• Existing methods for identifying vital nodes often require global network knowledge and assume static network structures, limiting their practical application.
• Identifying vital nodes is crucial for efficient network manipulation, information dissemination control, and disease outbreak management.

Purpose of the Study:

• To propose a novel localized strategy for identifying vital nodes in complex networks.
• To evaluate the effectiveness of the proposed strategy in diverse network datasets.
• To demonstrate the practical utility of the strategy in scenarios like epidemic control.

Main Methods:

• Developed a joint nomination (JN) strategy that identifies vital nodes using only local network information.
• Conducted experiments on 12 diverse network datasets (synthetic, real-world, directed, undirected).
• Assessed the impact of vital node removal on network properties (average shortest path length) and simulated disease spread (epidemic threshold, infection scale).

Main Results:

• The JN strategy identified node sets with 3-8 times higher average degrees compared to the full node set.
• Removal of JN-identified vital nodes significantly increased average shortest path length (20-70%) and improved immunization efficiency (12-40% resource saving).
• Simulations showed that removing 30% of vital nodes identified by JN delayed infection peaks and reduced total infection scale by 15%.

Conclusions:

• The proposed localized joint nomination (JN) strategy effectively identifies vital nodes in complex networks using only local information.
• JN is a practical and efficient method for network analysis and intervention, particularly in dynamic, real-world scenarios.
• The strategy offers significant advantages for managing spreading phenomena and responding to time-critical events like pandemics.