Evidential Reconstruction of Network from Time Series
Fuente:
arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866908861138468864 |
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| author | Xian, Yishu Zhang, Zhaobo Zhang, Cai Li, Meizhu Zhang, Qi |
| author_facet | Xian, Yishu Zhang, Zhaobo Zhang, Cai Li, Meizhu Zhang, Qi |
| contents | Reconstructing the topology of complex networks from observational data remains a central challenge in network science. Here we propose a framework that is based on the Dempster-Shafer evidence theory to infer network structures directly from time series. By integrating multi-source information within an evidential reasoning scheme, the method captures underlying interaction patterns with high fidelity. Tests on three representative network models Barabasi-Albert Network, Erdos-Renyi Network, and Watts-Strogatz Network-show that the reconstruction accuracy is consistently high and remains robust against increases in network size and density. The application of the framework to real-world datasets from diverse domains further confirms its stability and general applicability. These results suggest that evidential reasoning offers a powerful and scalable approach for uncovering the structural organization of complex systems, especially when dealing with uncertain or incomplete multi-source data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_02242 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Evidential Reconstruction of Network from Time Series Xian, Yishu Zhang, Zhaobo Zhang, Cai Li, Meizhu Zhang, Qi Physics and Society Classical Physics Data Analysis, Statistics and Probability Reconstructing the topology of complex networks from observational data remains a central challenge in network science. Here we propose a framework that is based on the Dempster-Shafer evidence theory to infer network structures directly from time series. By integrating multi-source information within an evidential reasoning scheme, the method captures underlying interaction patterns with high fidelity. Tests on three representative network models Barabasi-Albert Network, Erdos-Renyi Network, and Watts-Strogatz Network-show that the reconstruction accuracy is consistently high and remains robust against increases in network size and density. The application of the framework to real-world datasets from diverse domains further confirms its stability and general applicability. These results suggest that evidential reasoning offers a powerful and scalable approach for uncovering the structural organization of complex systems, especially when dealing with uncertain or incomplete multi-source data. |
| title | Evidential Reconstruction of Network from Time Series |
| topic | Physics and Society Classical Physics Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2603.02242 |