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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2512.08209 |
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| _version_ | 1866909950686527488 |
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| author | Zhang, Jiu Du, Zhanwei Hu, Hongwei Wu, Ke Li, Tongchao Shi, Chuan Huang, Xiaohui Moreno, Yamir Hu, Yanqing |
| author_facet | Zhang, Jiu Du, Zhanwei Hu, Hongwei Wu, Ke Li, Tongchao Shi, Chuan Huang, Xiaohui Moreno, Yamir Hu, Yanqing |
| contents | The dynamical evolution of complex networks underpins the structure-function relationships in natural and artificial systems. Yet, restoring a network's formation from a single static snapshot remains challenging. Here, we present a transferable machine learning framework that infers network evolutionary trajectories solely from present topology. By integrating graph neural networks with transformers, our approach unlocks a latent temporal dimension directly from the static topology. Evaluated across diverse domains, the framework achieves high transfer accuracy of up to 95.3%, demonstrating its robustness and transferability. Applied to the Drosophila brain connectome, it restores the formation times of over 2.6 million neural connections, revealing that early-forming links support essential behaviors such as mating and foraging, whereas later-forming connections underpin complex sensory and social functions. These results demonstrate that a substantial fraction of evolutionary information is encoded within static network architecture, offering a powerful, general tool for elucidating the hidden temporal dynamics of complex systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_08209 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Restoring Network Evolution from Static Structure Zhang, Jiu Du, Zhanwei Hu, Hongwei Wu, Ke Li, Tongchao Shi, Chuan Huang, Xiaohui Moreno, Yamir Hu, Yanqing Physics and Society The dynamical evolution of complex networks underpins the structure-function relationships in natural and artificial systems. Yet, restoring a network's formation from a single static snapshot remains challenging. Here, we present a transferable machine learning framework that infers network evolutionary trajectories solely from present topology. By integrating graph neural networks with transformers, our approach unlocks a latent temporal dimension directly from the static topology. Evaluated across diverse domains, the framework achieves high transfer accuracy of up to 95.3%, demonstrating its robustness and transferability. Applied to the Drosophila brain connectome, it restores the formation times of over 2.6 million neural connections, revealing that early-forming links support essential behaviors such as mating and foraging, whereas later-forming connections underpin complex sensory and social functions. These results demonstrate that a substantial fraction of evolutionary information is encoded within static network architecture, offering a powerful, general tool for elucidating the hidden temporal dynamics of complex systems. |
| title | Restoring Network Evolution from Static Structure |
| topic | Physics and Society |
| url | https://arxiv.org/abs/2512.08209 |