reg.SP: clusters nodes by regularized spectral clustering

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

clusters nodes by regularized spectral clustering

Description

community detection by regularized spectral clustering

Usage

reg.SP(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. It is shown to give consistent clustering under SBM.

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

K. Rohe, S. Chatterjee, and B. Yu. Spectral clustering and the high-dimensional stochastic blockmodel. The Annals of Statistics, pages 1878-1915, 2011.

A. A. Amini, A. Chen, P. J. Bickel, and E. Levina. Pseudo-likelihood methods for community detection in large sparse networks. The Annals of Statistics, 41(4):2097-2122, 2013.

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

C. M. Le, E. Levina, and R. Vershynin. Concentration and regularization of random graphs. Random Structures & Algorithms, 2017.

See Also

reg.SP

Examples

dt <- BlockModel.Gen(30,300,K=3,beta=0.2,rho=0)


A <- dt$A


sc <- reg.SP(A,K=3,lap=TRUE)


NMI(sc$cluster,dt$g)

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