Fig 3. Effects of spectral radius ρ, connection probability p and network size N.

Here we consider the case of noise-free covariance matrices, which were created based on the theory of the underlying model. The quantities considered are the area under the ROC curve (AUC; green), the precision recall score (PRS; orange) and the Pearson correlation coefficient (PCC; purple). The shaded areas indicate the mean ± standard deviation computed over 20 realizations. A If the network interaction is larger than ρ_min, it has relatively little effect on the performance of the estimation. Even in the extreme case, where ρ is close to 1, the estimation works well. B Performance of the estimation for different sparsity levels, encoded by the respective connection probabilities p. As expected, for non-sparse networks the performance of the algorithm degrades dramatically. C Performance of the estimation for increasing network size. Our results indicate clearly that bigger networks can be better reconstructed. Applicability may be limited by the numerical effort associated with the optimization. D Performance of the estitmation in presence of weak background connections. It is nevertheless possible to infer the skeleton of strong connections with high fidelity.