NetworkClustering: Network Clustering

In FCPS: Fundamental Clustering Problems Suite

Network Clustering

Description

Either leiden [Traag et al., 2019] or louvain [Blondel et al., 2008] clustering

Usage

NetworkClustering(DataOrDistances=NULL,Adjacency=NULL,

Type="louvain",Radius=FALSE,PlotIt=FALSE,...)

Arguments

DataOrDistances	NULL or: [1:n,1:d] matrix of dataset to be clustered. It consists of n cases or d-dimensional data points. Every case has d attributes, variables or features. Alternatively, symmetric [1:n,1:n] distance matrix
Adjacency	Used if DataOrDistances is NULL, matrix [1:n,1:n] defining which points are adjacent to each other by the number 1; not adjacent: 0
Type	Either "louvain" or "leiden"
Radius	Scalar, Radius for unit disk graph (r-ball graph) if adjacency matrix is missing. Automatic estimation can be done either with =TRUE [Ultsch, 2005] or FALSE [Thrun et al., 2016]
PlotIt	Default: FALSE, If TRUE plots the first three dimensions of the dataset with colored three-dimensional data points defined by the clustering stored in Cls
...	Further arguments to be set for the clustering algorithm, if not set, default arguments are used.

Details

DataOrDistances is used to compute the Adjecency matrix if this input is missing. Then a unit-disk (R-ball) graph is calculated. Radius=TRUE only works if data matrix is given.

Value

List of

Cls	[1:n] numerical vector with n numbers defining the classification as the main output of the clustering algorithm. It has k unique numbers representing the arbitrary labels of the clustering. Points which cannot be assigned to a cluster will be reported with 0.
Object	Object defined by clustering algorithm as the other output of this algorithm

Note

leiden requires igraph package and an installed python version. automatic installation may not work. manual call in console has to be in this case conda install -c conda-forge leidenalg

Author(s)

Michael Thrun

References

[Blondel et al., 2008] Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E.: Fast unfolding of communities in large networks, Journal of statistical mechanics: theory and experiment, Vol. 2008(10), pp. P10008. 2008.

[Traag et al., 2019] Traag, V. A., Waltman, L., & van Eck, N. J.: From Louvain to Leiden: guaranteeing well-connected communities, Scientific reports, Vol. 9(1), pp. 1-12. 2019.

Examples

data('Hepta')
#out=NetworkClustering(Hepta$Data,PlotIt=FALSE)

FCPS documentation built on Oct. 19, 2023, 5:06 p.m.