Identification of important nodes in the information propagation network based on the artificial intelligence method
This work addresses network analysis and optimization for strategic applications in domains such as social and communication systems, but it appears incremental as it builds on existing methods.
The study tackled the problem of identifying key nodes in information propagation networks by introducing an integrated AI method combining DEMATEL and GSM, applied to complex networks like social and transportation systems, and demonstrated its effectiveness in providing a comprehensive understanding of network behavior.
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.