Science Advances

Supplementary Materials

This PDF file includes:

  • Section S1. Deriving the linear system minimizing the Hamiltonian
  • Section S2. Poisson generative model
  • Section S3. Rewriting the energy
  • Section S4. Ranks distributed as a multivariate Gaussian distribution
  • Section S5. Bayesian SpringRank
  • Section S6. Fixing c to control for sparsity
  • Section S7. Comparing optimal β for predicting edge directions
  • Section S8. Bitwise accuracy σb
  • Section S9. Performance metrics
  • Section S10. Parameters used for regularizing ranking methods
  • Section S11. Supplementary tables
  • Section S12. Supplementary figures
  • Table S1. Pearson correlation coefficients between various rankings of faculty hiring networks.
  • Table S2. Statistics for SpringRank applied to real-world networks.
  • Fig. S1. Performance (Pearson correlation) on synthetic data.
  • Fig. S2. Statistical significance testing using the null model distribution of energies.
  • Fig. S3. Edge prediction accuracy over BTL for NCAA basketball data sets.
  • Fig. S4. Bitwise edge direction prediction.
  • Fig. S5. Edge prediction accuracy with twofold cross-validation.
  • Fig. S6. Summary of SpringRank applied to computer science faculty hiring network.
  • Fig. S7. Summary of SpringRank applied to history faculty hiring network.
  • Fig. S8. Summary of SpringRank applied to business faculty hiring network.
  • Fig. S9. Summary of SpringRank applied to Asian elephants network.
  • Fig. S10. Summary of SpringRank applied to parakeet G1 network.
  • Fig. S11. Summary of SpringRank applied to parakeet G2 network.
  • Fig. S12. Summary of SpringRank applied to Tenpaṭṭi social support network.
  • Fig. S13. Summary of SpringRank applied to Alakāpuram social support network.
  • Reference (46)

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