Spreading predictability in complex networks
Abstract Many state-of-the-art researches focus on predicting infection scale or threshold in infectious diseases or rumor and give the vaccination strategies correspondingly. In these works, most of them assume that the infection probability and initially infected individuals are known at the very...
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| Autores principales: | , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Nature Portfolio
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/e4ff672e9b7b4fb8b4fb6ec569b66632 |
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| Sumario: | Abstract Many state-of-the-art researches focus on predicting infection scale or threshold in infectious diseases or rumor and give the vaccination strategies correspondingly. In these works, most of them assume that the infection probability and initially infected individuals are known at the very beginning. Generally, infectious diseases or rumor has been spreading for some time when it is noticed. How to predict which individuals will be infected in the future only by knowing the current snapshot becomes a key issue in infectious diseases or rumor control. In this report, a prediction model based on snapshot is presented to predict the potentially infected individuals in the future, not just the macro scale of infection. Experimental results on synthetic and real networks demonstrate that the infected individuals predicted by the model have good consistency with the actual infected ones based on simulations. |
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