fitSBMcollection: Fit a unique stochastic block model to a collection of...

In graphclust: Hierarchical Graph Clustering for a Collection of Networks

Fit a unique stochastic block model to a collection of networks

Description

fitSBMcollection() is a subversion of graphClustering() where no stopping criterion is applied. So all networks are ultimately merged to a single cluster and considered as i.i.d realisations of a single stochastic block model.

Usage

fitSBMcollection(
  allAdj,
  hyperParam = list(alpha = 0.5, eta = 0.5, zeta = 0.5, lambda = 0.5),
  nbCores = 1
)

Arguments

allAdj	list of adjacency matrices
hyperParam	hyperparameters of prior distributions
nbCores	number of cores for parallelization

Value

list with the following fields: $nodeClusterings is a list with the node labels for each networks, $theta contains the estimated SBM parameter, $ICL is the value of the ICL criterion of the final clustering

Examples

theta <- list(pi=c(.5,.5), gamma=matrix((1:4)/8,2,2))
obs <- rCollectSBM(rep(10,4), theta)$listGraphs
res <- fitSBMcollection(obs, nbCores=1)

graphclust documentation built on June 7, 2023, 5:18 p.m.