The Conditional Pseudo-Likelihood (CPL) algorithm for fitting degree-corrected block models
fast_cpl(Amat, K, ilabels = NULL, niter = 10)Estimated community label vector.
adjacency matrix of the network
desired number of communities
initial label vector (if not provided, initial labels are estimated using spec_clust)
number of iterations
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.
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.
head(fast_cpl(igraph::as_adj(polblogs), 2), 50)
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