A multi-attribute method for ranking influential nodes in complex networks.

Area of Science:

• Network Science
• Computational Social Science
• Information Theory

Background:

• Ranking influential nodes in complex networks is a critical challenge.
• Existing methods often rely on single structural attributes, limiting accuracy across diverse network structures.
• Accurate node influence assessment is vital for understanding and predicting information diffusion.

Purpose of the Study:

• To develop a robust method for identifying and ranking node diffusion power in complex networks.
• To address the limitations of single-attribute influence measures by incorporating multiple node characteristics.
• To present a multi-attribute decision-making approach for node classification based on information spreading capabilities.

Main Methods:

• Utilized a multi-attribute decision-making approach combining local and semi-local network attributes.
• Employed attributes with linear time complexity for efficient computation.
• Evaluated the method on real-world network datasets to assess performance.

Main Results:

• The proposed method effectively assigns distinct ranks to nodes based on their diffusion power.
• Performance evaluation on real networks demonstrated satisfactory ranking accuracy.
• Accuracy of the node influence ranking increases with higher infection rates.

Conclusions:

• A multi-attribute approach offers a more accurate and adaptable solution for ranking node influence in complex networks.
• The developed method provides a reliable tool for identifying key spreaders of information.
• Future work could explore further attribute combinations and network types.