  This study presents an integrated approach for identifying key nodes in
information propagation networks using advanced artificial intelligence
methods. We introduce a novel technique that combines the Decision-making Trial
and Evaluation Laboratory (DEMATEL) method with the Global Structure Model
(GSM), creating a synergistic model that effectively captures both local and
global influences within a network. This method is applied across various
complex networks, such as social, transportation, and communication systems,
utilizing the Global Network Influence Dataset (GNID). Our analysis highlights
the structural dynamics and resilience of these networks, revealing insights
into node connectivity and community formation. The findings demonstrate the
effectiveness of our AI-based approach in offering a comprehensive
understanding of network behavior, contributing significantly to strategic
network analysis and optimization.
