Synergistic Integration of Local and Global Information for Critical Edge Identification

Area of Science:

• Network Science
• Graph Theory
• Data Analysis

Background:

• Identifying critical edges in complex networks is challenging.
• Traditional methods using only local or global information are insufficient.
• A comprehensive approach is needed for accurate critical edge identification.

Purpose of the Study:

• To develop an integrated method for enhanced critical edge identification.
• To improve understanding and optimization of complex network structures and functions.
• To address the limitations of single-source information in network analysis.

Main Methods:

• Introduced the Global-Local Hybrid Centrality method.
• Integrated second-order neighborhood index, first-order neighborhood index, and edge betweenness index.
• Utilized edge percolation process to assess edge significance for network connectivity.

Main Results:

• The Global-Local Hybrid Centrality method significantly improves critical edge identification accuracy.
• Experimental results on real-world datasets validate the method's effectiveness.
• The approach successfully combines local and global network perspectives.

Conclusions:

• The proposed method offers a more accurate way to identify critical edges in complex networks.
• This provides theoretical and methodological support for complex network analysis and optimization.
• Integrating diverse information sources is key to understanding network complexity.