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graphclust (version 1.3)

Hierarchical Graph Clustering for a Collection of Networks

Description

Graph clustering using an agglomerative algorithm to maximize the integrated classification likelihood criterion and a mixture of stochastic block models. The method is described in the article "Model-based clustering of multiple networks with a hierarchical algorithm" by T. Rebafka (2022) .

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Version

Install

install.packages('graphclust')

Monthly Downloads

201

Version

1.3

License

GPL-2

Maintainer

Tabea Rebafka

Last Published

June 7th, 2023

Functions in graphclust (1.3)

rsbm

Simulate a network of a stochastic block model
sampleDPA

generation of a network of the directed preferential attachment (DPA) model
permutParam

Permute block labels of a stochastic block model parameter
plotDendrogram

Plot dendrogram to visualize the clustering obtained by the hierarchical clustering algorithm
fitSimpleSBM

Fit a stochastic block model to every network in a collection of networks.
fitSBMcollection

Fit a unique stochastic block model to a collection of networks
ARI

Adjusted Rand index
graphMomentsClustering

Graph clustering method using graph moments
graphonL2norm

(squared) L2-norm of the graphons associated with two stochastic block model parameters
rCollectSBM

Simulate a sample of networks of a stochastic block model
graphonSpectralClustering

Graph clustering using the pairwise graphon distances and spectral clustering
sampleDPAMixture

Generation of a mixture of directed preferential attachment (DPA) models
degreeSort

Sort stochastic block model parameter in a unique way using its graphon
metagraph

Plot the metagraph of the parameter of the stochastic block model associated with one of the estimated graph clusters
moments

Computation of graph moments of a network
sbmNorm

(squared) norm between two stochastic block models
graphClustering

Hierarchical graph clustering algorithm
rMixSBM

Simulate a collection of networks of a mixture of stochastic block models