Inductive Node-Splitting Cross-Validation for Community
Detection
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Documentation for package ‘INCVCommunityDetection’ version 0.1.0
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| AUC | Negative AUC for matrix predictions |
| best.perm.label.match | Find the best permutation label matching |
| bin.dev | Binomial deviance loss |
| blockmodel.gen.fast | Generate a fast SBM or DCBM network (sparse) |
| community.sim | Simulate a Stochastic Block Model network |
| community.sim.sbm | Simulate an SBM with distance-decaying block probabilities |
| croissant.blockmodel | CROISSANT for blockmodel selection |
| croissant.latent | CROISSANT for latent space model dimension selection |
| croissant.rdpg | CROISSANT for RDPG rank selection |
| croissant.tune.regsp | CROISSANT for regularisation parameter tuning in spectral methods |
| ECV.for.blockmodel | Edge Cross-Validation for blockmodel selection |
| ECV.undirected.Rank | Edge Cross-Validation for RDPG rank selection |
| edge.index.map | Map a linear edge index to row-column indices in the upper triangle |
| eigen.DCBM.est | Eigenvector-based DCBM estimation |
| fast.DCBM.est | Fast DCBM parameter estimation |
| fast.SBM.est | Fast SBM block probability estimation |
| l2 | L2 loss between two matrices |
| latent.gen | Generate a latent space network |
| matched.lab | Apply label permutation to match reference |
| NCV.for.blockmodel | Node Cross-Validation for blockmodel selection |
| neglog | Safe negative log-likelihood term |
| nscv.f.fold | Inductive Node-Splitting Cross-Validation with f-fold splitting |
| nscv.random.split | Inductive Node-Splitting Cross-Validation with random node splits |
| SBM.prob | Estimate SBM connection probabilities and negative log-likelihood |
| SBM.spectral.clustering | Spectral clustering for a Stochastic Block Model |
| sparse.RDPG.gen | Generate a sparse RDPG network |