This study introduces an integrated approach to identify important nodes in information propagation networks using advanced artificial intelligence methods. By combining the DEMATEL method with the Global Structure Model (GSM), the authors create a synergistic model that captures both local and global influences within various complex networks. The analysis conducted on social, transportation, and communication systems utilizing the Global Network Influence Dataset (GNID) reveals insights into node connectivity, community formation, and network dynamics. The findings demonstrate the effectiveness of AI-based approaches in strategic network analysis and optimization. The paper critiques traditional methods like degree centrality and eigenvector centrality for their limitations in integrating local and global network information effectively. It proposes a novel approach that leverages AI capabilities to address these challenges, offering valuable insights for strategic planning and network optimization.
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by Bin Yuan,Tia... at arxiv.org 03-04-2024
https://arxiv.org/pdf/2403.00190.pdfDeeper Inquiries