The first step toward advancing our understanding of complex networks involves determining their connectivity structures from the time series data. These networks are often high-dimensional, and in practice, only a limited amount of data can be collected. In this work, we formulate the network inference task as a bilinear optimization problem and propose an iterative algorithm with sequential initialization to solve this bilinear program. We demonstrate the scalability of our approach to network size and its robustness against measurement noise, hyper-parameter variation, and deviations from the network model. Results across experimental and simulated datasets, comprising oscillatory, non-oscillatory, and chaotic dynamics, showcase the superior inference accuracy of our technique compared to existing methods.
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Research Article|
May 03 2024
Identification of network interactions from time series data: An iterative approach
Special Collection:
Data-Driven Models and Analysis of Complex Systems
Bharat Singhal
;
Bharat Singhal
(Conceptualization, Formal analysis, Investigation, Writing – original draft, Writing – review & editing)
1
Department of Electrical and Systems Engineering, Washington University in St Louis
, St Louis, Missouri 63130, USA
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Shicheng Li
;
Shicheng Li
(Formal analysis, Investigation, Writing – review & editing)
2
Department of Mechanical Engineering and Materials Science, Washington University in St Louis
, St Louis, Missouri 63130, USA
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Jr-Shin Li
Jr-Shin Li
a)
(Conceptualization, Writing – review & editing)
1
Department of Electrical and Systems Engineering, Washington University in St Louis
, St Louis, Missouri 63130, USA
a)Author to whom correspondence should be addressed: jsli@wustl.edu
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a)Author to whom correspondence should be addressed: jsli@wustl.edu
Chaos 34, 053114 (2024)
Article history
Received:
March 25 2024
Accepted:
April 18 2024
Citation
Bharat Singhal, Shicheng Li, Jr-Shin Li; Identification of network interactions from time series data: An iterative approach. Chaos 1 May 2024; 34 (5): 053114. https://doi.org/10.1063/5.0210115
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