A graph-traversal approach to identify influential nodes in a network.

Area of Science:

• Network Science
• Graph Theory
• Data Analysis

Background:

• Identifying influential nodes is crucial for understanding network structure and function.
• Existing centrality measures may not fully capture node influence in complex networks.

Purpose of the Study:

• To propose a general and effective method for influential node identification.
• To evaluate the proposed method against established centrality measures.

Main Methods:

• A graph-traversal framework utilizing breadth-first search (BFS) trees.
• Calculating node influence based on the area under a cumulative score curve derived from BFS trees.
• Evaluating the method across diverse network datasets.

Main Results:

• The proposed method yields a robust influence score for each node.
• Experimental results show the method's superiority over widely used centrality measures.
• The approach demonstrates effectiveness across various network domains.

Conclusions:

• The novel graph-traversal method provides a superior approach to influential node detection.
• This technique enhances the understanding of network structure and function.
• The method offers a valuable tool for analyzing complex systems.