graphClustering function

Hierarchical graph clustering algorithm

Applies the hierarchical graph clustering algorithm to a collection of networks and fits a finite mixture model of stochastic block models to the data

graphClustering(
  allAdj,
  hyperParam = list(alpha = 0.5, eta = 0.5, zeta = 0.5, lambda = 0.5),
  returnInitial = FALSE,
  nbClust = NULL,
  nbSBMBlocks = Inf,
  initCountStat = NULL,
  initDeltaICL = NULL,
  nbCores = 1
)

Arguments

• allAdj: list of adjacency matrices
• hyperParam: hyperparameters of prior distributions
• returnInitial: Boolean. Return SBM parameters from initialization or not. Default is FALSE.
• nbClust: desired number of clusters. Default NULL, which means that the number of clusters is chosen automatically via the ICL criterion
• nbSBMBlocks: upper bound for the number of blocks in the SBMs of the mixture components. Default is Inf
• initCountStat: initial count statistics may be provided to the method. Default is NULL.
• initDeltaICL: initial deltaICL-matrix may be provided to the method. Default is NULL.
• nbCores: number of cores for parallelization

Returns

list with the following fields: $graphGroups is the graph clustering,$nodeClusterings is a list with the node labels for each networks, $thetaMixSBM contains the estimated parameter of the mixture of SBMs,$ICL is the value of the ICL criterion of the final clustering, $histGraphGroups traces the history of the cluster aggregations,$histDeltaICL traces the evolution of the deltaICL value, $histFusedClusters traces the history of the aggregated cluster numbers

Examples

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