Identification of important nodes in the information propagation network based on the artificial intelligence method
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866909124093018112 |
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| author | Yuan, Bin Song, Tianbo Yao, Jerry |
| author_facet | Yuan, Bin Song, Tianbo Yao, Jerry |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_00190 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Identification of important nodes in the information propagation network based on the artificial intelligence method Yuan, Bin Song, Tianbo Yao, Jerry Social and Information Networks Artificial Intelligence 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. |
| title | Identification of important nodes in the information propagation network based on the artificial intelligence method |
| topic | Social and Information Networks Artificial Intelligence |
| url | https://arxiv.org/abs/2403.00190 |