reg.SSP: detects communities by regularized spherical spectral...

In randnet: Random Network Model Estimation, Selection and Parameter Tuning

detects communities by regularized spherical spectral clustering

Description

community detection by regularized spherical spectral clustering

Usage

reg.SSP(A, K, tau = 1, lap = FALSE,nstart=30,iter.max=100)

Arguments

A	adjacency matrix
K	number of communities
tau	reguarlization parameter. Default value is one. Typically set between 0 and 1. If tau=0, no regularization is applied.
lap	indicator. If TRUE, the Laplacian matrix for clustering. If FALSE, the adjacency matrix will be used.
nstart	number of random initializations for K-means
iter.max	maximum number of iterations for K-means

Details

The regularlization is done by adding a small constant to each element of the adjacency matrix. It is shown by such perturbation helps concentration in sparse networks. The difference from spectral clustering (reg.SP) comes from its extra step to normalize the rows of spectral vectors. It is proved that it gives consistent clustering under DCSBM.

Value

a list of

cluster	cluster labels
loss	the loss of Kmeans algorithm

Author(s)

Tianxi Li, Elizaveta Levina, Ji Zhu

Maintainer: Tianxi Li <[e-mail]>

References

T. Qin and K. Rohe. Regularized spectral clustering under the degree-corrected stochastic blockmodel. In Advances in Neural Information Processing Systems, pages 3120-3128, 2013.

J. Lei and A. Rinaldo. Consistency of spectral clustering in stochastic block models. The Annals of Statistics, 43(1):215-237, 2014.

See Also

reg.SP

Examples

dt <- BlockModel.Gen(30,300,K=3,beta=0.2,rho=0.9,simple=FALSE,power=TRUE)


A <- dt$A

ssc <- reg.SSP(A,K=3,lap=TRUE)

NMI(ssc$cluster,dt$g)

randnet documentation built on May 31, 2023, 6:44 p.m.