Identifying influential nodes: A new method based on network efficiency of edge weight updating

Area of Science:

• Network Science
• Complex Systems Analysis

Background:

• Identifying influential nodes is vital across disciplines like medicine, sociology, and engineering.
• Traditional methods often rely on limited local or global network information, leading to inaccuracies.
• A need exists for more accurate methods that leverage comprehensive network data.

Purpose of the Study:

• To propose a novel method for identifying influential nodes in complex networks.
• To enhance accuracy by integrating both local and global network information.
• To introduce dynamic network properties through iterative weight updating.

Main Methods:

• A new identification method based on network efficiency of edge weight updating.
• Incorporation of both global and local network information.
• Introduction of iterative weight updating to capture dynamic network characteristics.

Main Results:

• The proposed method effectively combines global and local network information.
• It avoids information loss inherent in traditional approaches.
• Experimental validation on 11 real-world datasets demonstrates superior performance.

Conclusions:

• The proposed edge weight updating method offers a more accurate approach to identifying influential nodes.
• Integrating local, global, and dynamic information improves node influence identification.
• This method shows significant effectiveness and superiority over existing techniques.