Enhanced complex network influential node detection through the integration of entropy and degree metrics with node...

Area of Science:

• Network Science
• Computational Social Science

Background:

• Complex networks are fundamental to systems like marketing, transportation, and epidemic modeling.
• Identifying influential nodes is key for optimizing processes and preventing negative outcomes.
• Current methods like Degree Centrality and K-shell have limitations in accuracy and computational efficiency.

Purpose of the Study:

• To propose a novel and efficient method for identifying influential nodes in complex networks.
• To overcome the limitations of existing methods by integrating local and global network properties.
• To enhance the accuracy of influential node identification for various real-world applications.

Main Methods:

• Developed the Entropy Degree Distance Combination (EDDC) approach.
• Integrated local metrics (entropy) and global metrics (degree, distance, path information).
• Evaluated the EDDC method on six benchmark datasets using standard evaluation metrics.

Main Results:

• The EDDC method demonstrated superior efficiency in identifying influential nodes.
• The integration of local and global measures improved the accuracy of node ranking.
• The approach proved effective across diverse network structures and applications.

Conclusions:

• The EDDC method offers a significant advancement in influential node identification.
• This approach provides a more comprehensive understanding of network structures and node importance.
• EDDC has strong potential for applications in areas such as virus spread modeling and viral marketing.