Deep learning of contagion dynamics on complex networks
Prediction of contagion dynamics is of relevance for epidemic and social complex networks. Murphy et al. propose a data-driven approach based on deep learning which allows to learn mechanisms governing network dynamics and make predictions beyond the training data for arbitrary network structures.
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2021-08-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-021-24732-2 |
| _version_ | 1818994094453555200 |
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| author | Charles Murphy Edward Laurence Antoine Allard |
| author_facet | Charles Murphy Edward Laurence Antoine Allard |
| author_sort | Charles Murphy |
| collection | DOAJ |
| description | Prediction of contagion dynamics is of relevance for epidemic and social complex networks. Murphy et al. propose a data-driven approach based on deep learning which allows to learn mechanisms governing network dynamics and make predictions beyond the training data for arbitrary network structures. |
| first_indexed | 2024-12-20T20:52:29Z |
| format | Article |
| id | doaj.art-dd478dea3f164012a35d9b2dec8c9bb8 |
| institution | Directory Open Access Journal |
| issn | 2041-1723 |
| language | English |
| last_indexed | 2024-12-20T20:52:29Z |
| publishDate | 2021-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj.art-dd478dea3f164012a35d9b2dec8c9bb82022-12-21T19:26:54ZengNature PortfolioNature Communications2041-17232021-08-0112111110.1038/s41467-021-24732-2Deep learning of contagion dynamics on complex networksCharles Murphy0Edward Laurence1Antoine Allard2Département de physique, de génie physique et d’optique, Université LavalDépartement de physique, de génie physique et d’optique, Université LavalDépartement de physique, de génie physique et d’optique, Université LavalPrediction of contagion dynamics is of relevance for epidemic and social complex networks. Murphy et al. propose a data-driven approach based on deep learning which allows to learn mechanisms governing network dynamics and make predictions beyond the training data for arbitrary network structures.https://doi.org/10.1038/s41467-021-24732-2 |
| spellingShingle | Charles Murphy Edward Laurence Antoine Allard Deep learning of contagion dynamics on complex networks Nature Communications |
| title | Deep learning of contagion dynamics on complex networks |
| title_full | Deep learning of contagion dynamics on complex networks |
| title_fullStr | Deep learning of contagion dynamics on complex networks |
| title_full_unstemmed | Deep learning of contagion dynamics on complex networks |
| title_short | Deep learning of contagion dynamics on complex networks |
| title_sort | deep learning of contagion dynamics on complex networks |
| url | https://doi.org/10.1038/s41467-021-24732-2 |
| work_keys_str_mv | AT charlesmurphy deeplearningofcontagiondynamicsoncomplexnetworks AT edwardlaurence deeplearningofcontagiondynamicsoncomplexnetworks AT antoineallard deeplearningofcontagiondynamicsoncomplexnetworks |