Solving the inverse problem of noise-driven dynamic networks

Abstract

Nowadays massive amount of data are available for analysis in natural and social systems. Inferring system structures from the data, i.e., the inverse problem, has become one of the central issues in many disciplines and interdisciplinary studies. In this Letter, we study the inverse problem of stochastic dynamic complex networks. We derive analytically a simple and universal inference formula called double correlation matrix (DCM) method. Numerical simulations confirm that the DCM method can accurately depict both network structures and noise correlations by using available kinetic data only. This inference performance was never regarded possible by theoretical derivation, numerical computation and experimental design.

4 pages, 2 figures

References in corpus (3)

• Emergence of scaling in random networks
• Inferring Network Topology from Complex Dynamics
• Noise bridges dynamical correlation and topology in coupled oscillator networks

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• Reconstructing dynamics of complex systems from noisy time series with hidden variables
• Optimized two-dimensional Networks with edge crossing cost: frustrated anti-ferromagnetic spin system