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community_detect(): unified spectral community
detection for the stochastic block model (model = "sbm",
k-means on regularized Laplacian embedding) and the degree-corrected
stochastic block model (model = "dcsbm", spherical k-median
on row-normalized embedding). Implements the algorithms of Lei and
Rinaldo (2015).
estimate_K(): Bethe–Hessian spectral estimator for
the number of communities in sparse networks. Implements the method of
Hwang (2023).
simulate_sbm(), simulate_dcsbm():
simulation utilities for generating benchmark graphs under both
models.
misclustering_rate(): permutation-corrected
misclustering rate (Hungarian algorithm via clue, greedy
fallback otherwise).
plot_scree(): scree plot of regularized Laplacian
eigenvalues to guide selection of K.
plot() S3 method for "sparsecommunity"
objects: scatter plot of the spectral embedding colored by detected
community.
print() and summary() S3 methods for
"sparsecommunity", "sbm_sim", and
"dcsbm_sim" objects.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.