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Enhanced network inference from sparse incomplete time series through automatically adapted L1 regularization

Enhanced network inference from sparse incomplete time series through automatically adapted L1 regularization
Enhanced network inference from sparse incomplete time series through automatically adapted L1 regularization

Reconstructing dynamics of complex systems from sparse, incomplete time series data is a challenging problem with applications in various domains. Here, we develop an iterative heuristic method to infer the underlying network structure and parameters governed by Ising dynamics from incomplete spin configurations based on sparse and small-sized samples. Our method iterates between imputing missing spin states given current coupling strengths and re-estimating couplings from completed spin state data. Central to our approach is the novel application of adaptive l 1 regularization on updating coupling strengths, which features an automatic adjustment of the regularization strength throughout the iterative inference process. By doing so, we aim at preventing over-fitting and enforcing the sparsity of couplings without access to ground truth parameters. We demonstrate that this approach accurately recovers parameters and imputes missing spins even with substantial missing data and short time series, providing improvements in the inference of Ising model parameters even for relatively small sample sizes.

Complex networks, Inverse ising model, Network inference
Cai, Zhongqi
b3ce4c1b-e545-4a86-9592-960542756e14
Gerding, Enrico
d9e92ee5-1a8c-4467-a689-8363e7743362
Brede, Markus
bbd03865-8e0b-4372-b9d7-cd549631f3f7
Cai, Zhongqi
b3ce4c1b-e545-4a86-9592-960542756e14
Gerding, Enrico
d9e92ee5-1a8c-4467-a689-8363e7743362
Brede, Markus
bbd03865-8e0b-4372-b9d7-cd549631f3f7

Cai, Zhongqi, Gerding, Enrico and Brede, Markus (2024) Enhanced network inference from sparse incomplete time series through automatically adapted L1 regularization. Applied Network Science, 9 (1), [13]. (doi:10.1007/s41109-024-00621-7).

Record type: Article

Abstract

Reconstructing dynamics of complex systems from sparse, incomplete time series data is a challenging problem with applications in various domains. Here, we develop an iterative heuristic method to infer the underlying network structure and parameters governed by Ising dynamics from incomplete spin configurations based on sparse and small-sized samples. Our method iterates between imputing missing spin states given current coupling strengths and re-estimating couplings from completed spin state data. Central to our approach is the novel application of adaptive l 1 regularization on updating coupling strengths, which features an automatic adjustment of the regularization strength throughout the iterative inference process. By doing so, we aim at preventing over-fitting and enforcing the sparsity of couplings without access to ground truth parameters. We demonstrate that this approach accurately recovers parameters and imputes missing spins even with substantial missing data and short time series, providing improvements in the inference of Ising model parameters even for relatively small sample sizes.

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Accepted/In Press date: 22 April 2024
e-pub ahead of print date: 30 April 2024
Published date: December 2024
Additional Information: Publisher Copyright: © The Author(s) 2024.
Keywords: Complex networks, Inverse ising model, Network inference

Identifiers

Local EPrints ID: 489694
URI: http://eprints.soton.ac.uk/id/eprint/489694
PURE UUID: bc3fce92-9294-449a-a5c7-97c2fb05aa8e
ORCID for Enrico Gerding: ORCID iD orcid.org/0000-0001-7200-552X

Catalogue record

Date deposited: 30 Apr 2024 16:54
Last modified: 13 Jun 2024 01:39

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Contributors

Author: Zhongqi Cai
Author: Enrico Gerding ORCID iD
Author: Markus Brede

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