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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