Identification of Important Nodes Based on Local Effective Distance-Integrated Gravity Model

Area of Science:

• Complex network analysis
• Network science
• Graph theory

Background:

• Identifying critical nodes is a key challenge in complex network research.
• Current effective distance methods are computationally expensive and ignore node attributes.
• Overlooking node multi-attribute characteristics leads to inaccurate importance assessments.

Purpose of the Study:

• To develop a novel, efficient, and accurate method for identifying important nodes in complex networks.
• To address the limitations of existing effective distance-based approaches.
• To enhance node importance evaluation by integrating multi-attribute characteristics.

Main Methods:

• Proposes an improved effective distance fusion model for node identification.
• Utilizes an effective-influence node set to reduce redundant calculations.
• Incorporates local, global, positional, and clustering information to assess node propagation capabilities.

Main Results:

• The novel method achieves higher efficiency and accuracy in identifying important nodes.
• Reduced computational complexity compared to traditional effective distance methods.
• Comprehensive node importance assessment through multi-attribute integration.

Conclusions:

• The proposed method offers a more effective approach to identifying key nodes in complex networks.
• Integrating multi-attribute node characteristics significantly improves identification accuracy.
• This research contributes to advancing the field of complex network analysis.