  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.
