Identifying influential nodes based on the disassortativity and community structure of complex network

Area of Science:

• Network Science
• Complex Systems Analysis
• Computational Social Science

Background:

• Complex networks display significant heterogeneity, with a few nodes holding critical roles in decision-making, disease control, and population immunity.
• Accurate identification of these influential nodes is vital for understanding and managing network behavior.
• Existing methods for identifying influential nodes often overlook network structural properties like disassortativity.

Purpose of the Study:

• To introduce and validate novel metrics for identifying influential nodes in complex networks.
• To explore the relationship between node influence, network disassortativity, and community structure.
• To propose a new measure that combines disassortativity and community structure for enhanced node influence identification.

Main Methods:

• Introduction of the 'disassortativity of the node' (DoN) concept and its quantification using a step function.
• Development of the 'influential metric of node based on disassortativity and community structure' (mDC).
• Extensive experimental validation on synthetic and real-world networks, including robustness and disease infection simulations.

Main Results:

• The proposed DoN and mDC metrics demonstrate superior performance and efficiency compared to existing state-of-the-art centrality measures.
• These methods show particular effectiveness in non-disassortative networks and those with clear community structures.
• DoN and mDC exhibit high stability against network noise and data inaccuracies.

Conclusions:

• The DoN and mDC metrics offer a powerful new approach for identifying influential nodes in complex networks.
• These metrics are valuable tools for network analysis, particularly in applications sensitive to node influence and network topology.
• The findings highlight the importance of considering disassortativity and community structure for accurate influence assessment.