fast_cpl: CPL algorithm for community detection (fast)

In aaamini/nett: Network Analysis and Community Detection

CPL algorithm for community detection (fast)

Description

The Conditional Pseudo-Likelihood (CPL) algorithm for fitting degree-corrected block models

Usage

fast_cpl(Amat, K, ilabels = NULL, niter = 10)

Arguments

Amat	adjacency matrix of the network
K	desired number of communities
ilabels	initial label vector (if not provided, initial labels are estimated using spec_clust)
niter	number of iterations

Details

The function implements the CPL algorithm as described in the paper below. It relies on the mixtools package for fitting a mixture of multinomials to a block compression of the adjacency matrix based on the estimated labels and then reiterates.

Technically, fast_cpl fits a stochastic block model (SBM) conditional on the observed node degrees,to account for the degree heterogeneity within communities that is not modeled well in SBM. CPL can also be used to effectively estimate the parameters of the degree-corrected block model (DCSBM).

The code is an adaptation of the original R code by Aiyou Chen with slight simplifications.

Value

Estimated community label vector.

References

For more details, see Pseudo-likelihood methods for community detection in large sparse networks, A. A. Amini, A. Chen, P. J. Bickel, E. Levina, Annals of Statistics 2013, Vol. 41 (4), 2097—2122.

Examples

head(fast_cpl(igraph::as_adj(polblogs), 2), 50)

aaamini/nett documentation built on Nov. 12, 2022, 6:25 p.m.