Fig. 2. Evaluation scheme illustrating robust reconstruction.

(A) Representative adjacency matrix indicating network connectivity as defined by present (black) and absent (white) links. (B) ROC curve obtained by varying a threshold J_c separating links classified as existing $(|{\widehat{\mathit{J}}}_{\mathit{i}\mathit{j}}|\ge {\mathit{J}}_{\mathit{c}})$ from those classified as absent $(|{\widehat{\mathit{J}}}_{\mathit{i}\mathit{j}}|<{\mathit{J}}_{\mathit{c}})$ (see note S3). The AUC increases with decreasing noise level, with perfect ranking of reconstructed links in the limit of noiseless dynamics. Inset: The quality of network reconstruction, as specified by the AUC, increases with the number of driving-response experiments. (C) The number of experiments required for high-quality reconstruction (here, AUC > 0.95) increases sublinearly (compared to the dotted line) with network size and (inset) changes only weakly with the noise level. Data are shown for random networks of (default size) N = 50 Goodwin oscillators with a regular incoming degree of 4, a default noise level of 0.5, a default number of experiments of 25, and a number of sampled time points of 100; shading indicates SD across ensembles of network realizations.