graph.cem {statGraph}

Graph Clustering Expectation-Maximization (gCEM)

Description

graph.cem clusters graphs following an expectation-maximization algorithm based on the Kullback-Leibler divergence between the spectral densities of the graph and of the random graph model.

Usage

graph.cem(Graphs, model, k, max_iter = 10, ...)

Arguments

Graphs

a list of undirected graphs. If each graph has the attribute eigenvalues containing its eigenvalues , such values will be used to compute their spectral density.

model

a string that indicates one of the following random graph models: 'ER' (Erdos-Renyi random graph), 'GRG' (geometric random graph), 'KR' (k regular graph), 'WS' (Watts-Strogatz model), and 'BA' (Barabási-Albert model).

k

an integer specifying the number of clusters.

max_iter

the maximum number of expectation-maximization steps to execute.

...

Other relevant parameters for graph.param.estimator.

Value

A list with class 'statGraph' containing the following components:

method:

a string indicating the used method.

info:

a string showing details about the method.

data.name:

a string with the data's name(s).

cluster:

a vector of the same length of g containing the clusterization labels.

parameters:

a vector containing the estimated parameters for the groups. It has the length equals to k.

References

Celeux, Gilles, and Gerard Govaert. 'Gaussian parsimonious clustering models.' Pattern recognition 28.5 (1995): 781-793.

Sheather, S. J. and Jones, M. C. (1991). A reliable data-based bandwidth selection method for kernel density estimation. _Journal of the Royal Statistical Society series B_, 53, 683-690. http://www.jstor.org/stable/2345597.

Examples

 set.seed(1)
 g <- list()
 for(i in 1:2){
   g[[i]] <- igraph::sample_gnp(n=10, p=0.5)
 }
 for(i in 3:4){
   g[[i]] <- igraph::sample_gnp(n=10, p=1)
 }
 res <- graph.cem(g, model='ER', k=2, max_iter=1,eps=0.1)
 res