Efficient reconstruction of directed networks from noisy dynamics using stochastic force inference
1Department of Physics, National Changhua University of Education, Changhua 500, Taiwan, Republic of China and Department of Physics and Center for Complex Systems, National Central University, Chung-Li District, Taoyuan City 320, Taiwan, Republic of China.
Physical Review. E
|October 21, 2022
Summary
Researchers developed a method to reconstruct directed networks from time-series data, accurately identifying connection weights and noise. This approach works even with partial network observations, enabling hidden node inference.
Area of Science:
- Complex Systems
- Network Science
- Statistical Physics
Background:
- Coupled network dynamics are often influenced by external noise.
- Understanding network structure from observed dynamics is crucial in many scientific fields.
Purpose of the Study:
- To develop a method for reconstructing directed network structures from time-series data of node dynamics.
- To accurately infer connection weights and noise strengths within networks.
Main Methods:
- Utilizing the stochastic force inference method with a linear polynomial basis.
- Analyzing time-series data of coupled network dynamics under uncorrelated noise.
Main Results:
- A robust scheme for reconstructing directed network connection weights and noise strengths was derived.
- The method demonstrated high accuracy and efficiency in simulations for various network types.
- Successful reconstruction of connections among observed nodes was achieved even with incomplete network data.
Conclusions:
- The developed method provides an effective tool for network reconstruction from observational data.
- The approach is capable of inferring network properties and handling partial observability.
- It offers a way to estimate the influence of unobserved (hidden) nodes on the system.
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