Finding Influential Nodes in Multiplex Networks Using a Memetic Algorithm

Area of Science:

• Network Science
• Computational Social Science
• Complex Systems

Background:

• Influence maximization is crucial for understanding information diffusion.
• Existing models struggle with complex, multi-layered (multiplex) networks.
• Identifying influential nodes in realistic networks remains a challenge.

Purpose of the Study:

• To develop an extended influence spreading model for multiplex networks.
• To design a memetic algorithm for finding influential seeds across all layers.
• To address the limitations of current methods in complex network structures.

Main Methods:

• Designed an extended influence spreading model tailored for multiplex networks.
• Developed a memetic algorithm to efficiently identify influential seeds.
• Validated the approach using both synthetic and real-world network data.

Main Results:

• The proposed algorithm effectively identifies influential nodes in multiplex networks.
• Experimental results demonstrate the algorithm's superior performance.
• The method successfully finds seeds influential across all network layers.

Conclusions:

• The developed model and algorithm offer a robust solution for influence maximization in multiplex networks.
• Findings aid in identifying key propagators in complex social networks.
• Provides new tools for analyzing and understanding networked systems.