Science Advances

Supplementary Materials

This PDF file includes:

  • note S1. Details of the derivation of invariant-based reconstruction.
  • note S2. Error estimates for observables from sampled invariant density.
  • note S3. Reconstruction evaluation.
  • note S4. Moderate influence of link density.
  • note S5. Reconstructing homogeneous and heterogeneous networks.
  • note S6. Reconstruction of systems near fixed points.
  • note S7. Reconstruction of chaotic systems.
  • note S8. Performance compared with available standard baselines.
  • note S9. Distinguishing activating from inhibiting interactions.
  • note S10. The effect of missing information.
  • note S11. Model descriptions.
  • note S12. The effect of various driving conditions on reconstruction quality.
  • note S13. Compressed sensing.
  • fig. S1. Approximating the center of mass of invariant densities by the sample mean.
  • fig. S2. Sparser networks require fewer experiments for robust reconstruction.
  • fig. S3. Reconstruction is robust across network topologies.
  • fig. S4. The quality of reconstruction increases with the number of experiments
    for a network of genetic regulators.
  • fig. S5. Reconstruction of a network of Rössler oscillators exhibiting chaotic dynamics.
  • fig. S6. Comparison of reconstruction quality across different approaches.
  • fig. S7. Comparison of reconstruction quality against transfer entropy.
  • fig. S8. Separate reconstruction of activating and inhibiting interactions enhances the quality of reconstruction.
  • fig. S9. Quality of reconstruction (AUC score) decreases gradually with the fraction of hidden units in the network.
  • fig. S10. Quality of reconstruction increases as driving signals overcome noise and finite sampling effects.
  • References (48–50)

Download PDF

Files in this Data Supplement: