Reconstructing the evolution history of networked complex systems
Abstract The evolution processes of complex systems carry key information in the systems’ functional properties. Applying machine learning algorithms, we demonstrate that the historical formation process of various networked complex systems can be extracted, including protein-protein interaction, ec...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-04-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-024-47248-x |
| _version_ | 1827291462333628416 |
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| author | Junya Wang Yi-Jiao Zhang Cong Xu Jiaze Li Jiachen Sun Jiarong Xie Ling Feng Tianshou Zhou Yanqing Hu |
| author_facet | Junya Wang Yi-Jiao Zhang Cong Xu Jiaze Li Jiachen Sun Jiarong Xie Ling Feng Tianshou Zhou Yanqing Hu |
| author_sort | Junya Wang |
| collection | DOAJ |
| description | Abstract The evolution processes of complex systems carry key information in the systems’ functional properties. Applying machine learning algorithms, we demonstrate that the historical formation process of various networked complex systems can be extracted, including protein-protein interaction, ecology, and social network systems. The recovered evolution process has demonstrations of immense scientific values, such as interpreting the evolution of protein-protein interaction network, facilitating structure prediction, and particularly revealing the key co-evolution features of network structures such as preferential attachment, community structure, local clustering, degree-degree correlation that could not be explained collectively by previous theories. Intriguingly, we discover that for large networks, if the performance of the machine learning model is slightly better than a random guess on the pairwise order of links, reliable restoration of the overall network formation process can be achieved. This suggests that evolution history restoration is generally highly feasible on empirical networks. |
| first_indexed | 2024-04-24T12:38:29Z |
| format | Article |
| id | doaj.art-7ab3508da09844f59fe78c1165052da2 |
| institution | Directory Open Access Journal |
| issn | 2041-1723 |
| language | English |
| last_indexed | 2024-04-24T12:38:29Z |
| publishDate | 2024-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj.art-7ab3508da09844f59fe78c1165052da22024-04-07T11:24:05ZengNature PortfolioNature Communications2041-17232024-04-0115111110.1038/s41467-024-47248-xReconstructing the evolution history of networked complex systemsJunya Wang0Yi-Jiao Zhang1Cong Xu2Jiaze Li3Jiachen Sun4Jiarong Xie5Ling Feng6Tianshou Zhou7Yanqing Hu8School of Systems Science and Engineering, Sun Yat-sen UniversityDepartment of Statistics and Data Science, College of Science, Southern University of Science and TechnologyDepartment of Statistics and Data Science, College of Science, Southern University of Science and TechnologyDepartment of Data Analytics and Digitalisation, School of Business and Economics, Maastricht UniversityTencent Inc.Center for Computational Communication Research, Beijing Normal UniversityInstitute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR)School of Mathematics, Sun Yat-sen UniversityDepartment of Statistics and Data Science, College of Science, Southern University of Science and TechnologyAbstract The evolution processes of complex systems carry key information in the systems’ functional properties. Applying machine learning algorithms, we demonstrate that the historical formation process of various networked complex systems can be extracted, including protein-protein interaction, ecology, and social network systems. The recovered evolution process has demonstrations of immense scientific values, such as interpreting the evolution of protein-protein interaction network, facilitating structure prediction, and particularly revealing the key co-evolution features of network structures such as preferential attachment, community structure, local clustering, degree-degree correlation that could not be explained collectively by previous theories. Intriguingly, we discover that for large networks, if the performance of the machine learning model is slightly better than a random guess on the pairwise order of links, reliable restoration of the overall network formation process can be achieved. This suggests that evolution history restoration is generally highly feasible on empirical networks.https://doi.org/10.1038/s41467-024-47248-x |
| spellingShingle | Junya Wang Yi-Jiao Zhang Cong Xu Jiaze Li Jiachen Sun Jiarong Xie Ling Feng Tianshou Zhou Yanqing Hu Reconstructing the evolution history of networked complex systems Nature Communications |
| title | Reconstructing the evolution history of networked complex systems |
| title_full | Reconstructing the evolution history of networked complex systems |
| title_fullStr | Reconstructing the evolution history of networked complex systems |
| title_full_unstemmed | Reconstructing the evolution history of networked complex systems |
| title_short | Reconstructing the evolution history of networked complex systems |
| title_sort | reconstructing the evolution history of networked complex systems |
| url | https://doi.org/10.1038/s41467-024-47248-x |
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