Package: graphclust 1.3

graphclust: Hierarchical Graph Clustering for a Collection of Networks

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) <arxiv:2211.02314>.

Authors:Tabea Rebafka [aut, cre]

graphclust_1.3.tar.gz
graphclust_1.3.zip(r-4.5)graphclust_1.3.zip(r-4.4)graphclust_1.3.zip(r-4.3)
graphclust_1.3.tgz(r-4.4-any)graphclust_1.3.tgz(r-4.3-any)
graphclust_1.3.tar.gz(r-4.5-noble)graphclust_1.3.tar.gz(r-4.4-noble)
graphclust_1.3.tgz(r-4.4-emscripten)graphclust_1.3.tgz(r-4.3-emscripten)
graphclust.pdf |graphclust.html✨
graphclust/json (API)

# Install 'graphclust' in R:

install.packages('graphclust', repos = c('https://tabea17.r-universe.dev', 'https://cloud.r-project.org'))

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.43 score 27 scripts 215 downloads 18 exports 19 dependencies

Last updated 1 years agofrom:e813e9642e. Checks:OK: 7. Indexed: yes.

Target	Result	Date
Doc / Vignettes	OK	Oct 22 2024
R-4.5-win	OK	Oct 22 2024
R-4.5-linux	OK	Oct 22 2024
R-4.4-win	OK	Oct 22 2024
R-4.4-mac	OK	Oct 22 2024
R-4.3-win	OK	Oct 22 2024
R-4.3-mac	OK	Oct 22 2024

Exports:ARI degreeSort fitSBMcollection fitSimpleSBM graphClustering graphMomentsClustering graphonL2norm graphonSpectralClustering metagraph moments permutParam plotDendrogram rCollectSBM rMixSBM rsbm sampleDPA sampleDPAMixture sbmNorm

Dependencies:blockmodels class cli cluster cpp11 digest glue igraph lattice lifecycle magrittr MASS Matrix pkgconfig Rcpp RcppArmadillo rlang sClust vctrs

Citation

Readme and manuals

Help Manual

Help page	Topics
Adjusted Rand index	ARI
Sort stochastic block model parameter in a unique way using its graphon	degreeSort
Fit a unique stochastic block model to a collection of networks	fitSBMcollection
Fit a stochastic block model to every network in a collection of networks.	fitSimpleSBM
Hierarchical graph clustering algorithm	graphClustering
Graph clustering method using graph moments	graphMomentsClustering
(squared) L2-norm of the graphons associated with two stochastic block model parameters	graphonL2norm
Graph clustering using the pairwise graphon distances and spectral clustering	graphonSpectralClustering
Plot the metagraph of the parameter of the stochastic block model associated with one of the estimated graph clusters	metagraph
Computation of graph moments of a network	moments
Permute block labels of a stochastic block model parameter	permutParam
Plot dendrogram to visualize the clustering obtained by the hierarchical clustering algorithm	plotDendrogram
Simulate a sample of networks of a stochastic block model	rCollectSBM
Simulate a collection of networks of a mixture of stochastic block models	rMixSBM
Simulate a network of a stochastic block model	rsbm
generation of a network of the directed preferential attachment (DPA) model	sampleDPA
Generation of a mixture of directed preferential attachment (DPA) models	sampleDPAMixture
(squared) norm between two stochastic block models	sbmNorm

Package: graphclust 1.3

graphclust: Hierarchical Graph Clustering for a Collection of Networks

Citation

Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)