cluster_spectral: Spectral Graph Clustering

In HyperG: Hypergraphs in R

Description

Use spectral embedding to embed a graph into a lower dimension, then cluster the points using model based clustering. This results in a clustering of the vertices.

Usage

cluster_spectral(g, verbose = FALSE, adjust.diag = FALSE, laplacian = FALSE, 
   normalize = FALSE, scale.by.values = FALSE, vectors = "u", d = 12, ...)

Arguments

g	a graph.
verbose	logical. Whether to print to the screen as it goes.
adjust.diag	logical. Whether to set the diagonal of the adjacency matrix to degree/(n-1).
laplacian	logical. Whether to use the Laplacian rather than the adjacency matrix.
normalize	logical. Whether to normalize the matrix by D^1/2.
scale.by.values	Whether to scale the embedding vectors by the eigen vectors.
vectors	character. "u" or "v" or "uv". The latter is only appropriate for directed graphs.
d	embedding dimension.
...	arguments passed to Mclust.

Details

This first embeds the vertices into a d-dimensional space, using the adjacency matrix or the Laplacian. See ase for more information. It then applies Mclust to the resultant points to cluster.

Value

An object of class "Mclust".

Author(s)

David J. Marchette [e-mail]

References

Fraley C. and Raftery A. E. (2002) Model-based clustering, discriminant analysis and density estimation, _Journal of the American Statistical Association_, 97/458, pp. 611-631.

See Also

ase.

Examples

	P <- rbind(c(.2,.05),c(.05,.1))
	ns <- rep(50,2)
	set.seed(451)
	g <- sample_sbm(sum(ns),P,ns)
	cluster_spectral(g)

HyperG documentation built on March 4, 2021, 5:06 p.m.