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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.