Identifying influential nodes through hierarchical k-shell and extended neighborhood integration

Area of Science:

• Complex network analysis
• Network science
• Computational social science

Background:

• Identifying influential nodes is crucial for understanding complex networks.
• Existing methods face challenges in balancing accuracy and computational efficiency.
• Need for algorithms that integrate global and local network properties.

Purpose of the Study:

• Propose HKEN, an algorithm integrating hierarchical k-shell decomposition and extended neighborhood information.
• Enhance the accuracy and computational efficiency of influential node identification.
• Validate HKEN's performance against benchmark methods.

Main Methods:

• Optimized hierarchical k-shell decomposition with dynamic node weighting.
• Extended neighborhood range and introduced local clustering coefficient for transmission distance.
• Influence aggregation strategy using Jaccard similarity coefficient.

Main Results:

• HKEN demonstrated superior performance on 10 real-world networks.
• Achieved higher consistency with the SIR (Susceptible-Infected-Recovered) model outcomes.
• Enhanced the propagation capability of top-ranked influential nodes.

Conclusions:

• HKEN offers improved accuracy in identifying influential nodes.
• The algorithm effectively balances accuracy and computational efficiency.
• HKEN provides a robust approach for complex network analysis.