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Reconstructing networks from dynamics with correlated noise

Author

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  • Tam, H.C.
  • Ching, Emily S.C.
  • Lai, Pik-Yin

Abstract

Reconstructing the structure of complex networks from measurements of the nodes is a challenge in many branches of science. External influences are always present and act as a noise to the networks of interest. In this paper, we present a method for reconstructing networks from measured dynamics of the nodes subjected to correlated noise that cannot be approximated by a white noise. This method can reconstruct the links of both bidirectional and directed networks, the correlation time and strength of the noise, and also the relative coupling strength of the links when the coupling functions have certain properties. Our method is built upon theoretical relations between network structure and measurable quantities from the dynamics that we have derived for systems that have fixed point dynamics in the noise-free limit. Using these theoretical results, we can further explain the shortcomings of two common practices of inferring links for bidirectional networks using the Pearson correlation coefficient and the partial correlation coefficient.

Suggested Citation

  • Tam, H.C. & Ching, Emily S.C. & Lai, Pik-Yin, 2018. "Reconstructing networks from dynamics with correlated noise," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 502(C), pages 106-122.
    • Handle: RePEc:eee:phsmap:v:502:y:2018:i:c:p:106-122
    DOI: 10.1016/j.physa.2018.02.166
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    References listed on IDEAS

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    1. Steven H. Strogatz, 2001. "Exploring complex networks," Nature, Nature, vol. 410(6825), pages 268-276, March.
    2. Matthieu Gilson & Ruben Moreno-Bote & Adrián Ponce-Alvarez & Petra Ritter & Gustavo Deco, 2016. "Estimation of Directed Effective Connectivity from fMRI Functional Connectivity Hints at Asymmetries of Cortical Connectome," PLOS Computational Biology, Public Library of Science, vol. 12(3), pages 1-30, March.
    3. Robert J Prill & Robert Vogel & Guillermo A Cecchi & Grégoire Altan-Bonnet & Gustavo Stolovitzky, 2015. "Noise-Driven Causal Inference in Biomolecular Networks," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-16, June.
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