Robust reconstruction of sparse network dynamics
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866915621838520320 |
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| author | Pereira, Tiago Santos, Edmilson Roque dos van Strien, Sebastian |
| author_facet | Pereira, Tiago Santos, Edmilson Roque dos van Strien, Sebastian |
| contents | Reconstruction of the network interaction structure from multivariate time series is an important problem in multiple fields of science. This problem is ill-posed for large networks leading to the reconstruction of false interactions. We put forward the Ergodic Basis Pursuit (EBP) method that uses the network dynamics' statistical properties to ensure the exact reconstruction of sparse networks when a minimum length of time series is attained. We show that this minimum time series length scales quadratically with the node degree being probed and logarithmic with the network size. Our approach is robust against noise and allows us to treat the noise level as a parameter. We show the reconstruction power of the EBP in experimental multivariate time series from optoelectronic networks. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2308_06433 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Robust reconstruction of sparse network dynamics Pereira, Tiago Santos, Edmilson Roque dos van Strien, Sebastian Data Analysis, Statistics and Probability Dynamical Systems 37A25, 37N99, 37E05, 37M25, Reconstruction of the network interaction structure from multivariate time series is an important problem in multiple fields of science. This problem is ill-posed for large networks leading to the reconstruction of false interactions. We put forward the Ergodic Basis Pursuit (EBP) method that uses the network dynamics' statistical properties to ensure the exact reconstruction of sparse networks when a minimum length of time series is attained. We show that this minimum time series length scales quadratically with the node degree being probed and logarithmic with the network size. Our approach is robust against noise and allows us to treat the noise level as a parameter. We show the reconstruction power of the EBP in experimental multivariate time series from optoelectronic networks. |
| title | Robust reconstruction of sparse network dynamics |
| topic | Data Analysis, Statistics and Probability Dynamical Systems 37A25, 37N99, 37E05, 37M25, |
| url | https://arxiv.org/abs/2308.06433 |