linkcomm (version 1.0-11)

getClusterRelatedness: Hierarchichal Clustering of Link Communities

Description

This function hierarchically clusters the link communities themselves and returns an object of class hclust.

Usage

getClusterRelatedness(x, clusterids = 1:x$numbers[3], hcmethod = "ward.D", 
        cluster = TRUE, plot = TRUE, cutat = NULL, col = TRUE, 
        pal = brewer.pal(11, "Spectral"), labels = FALSE, plotcut = TRUE, 
        right = TRUE, verbose = TRUE, ...)

Arguments

x

An object of class linkcomm.

clusterids

An integer vector of community IDs. Defaults to all communities.

hcmethod

A character string naming the hierarchical clustering method to use. Can be one of "ward.D", "ward.D2", "single", "complete", "average", "mcquitty", "median", or "centroid". Defaults to "ward.D".

cluster

Logical, whether to cluster the communities. If FALSE, the function returns the upper triangular dissimilarity matrix as a vector. Defaults to TRUE.

plot

Logical, whether to plot the cluster dendrogram.

cutat

A numerical value at which to cut the dendrogram. If NULL, the dendrogram is not cut and meta-communities are not returned. Defaults to NULL.

col

Logical, whether to colour the dendrogram. Defaults to TRUE.

pal

A character vector describing a colour palette to be used for colouring the meta-communites in the dendrogram plot. Defaults to brewer.pal(11, "Spectral").

labels

Logical, whether to add labels to the dendrogram plot.

plotcut

Logical, whether to display a horizontal line where the dendrogram is cut. Defaults to TRUE.

right

Logical, whether to orient the dendrogram to the right. Defaults to TRUE.

verbose

Logical, whether to display the progress of the calculation on the screen. Defaults to TRUE.

…

Additional arguments to be passed to plot.

Value

Either a numerical vector (the upper triangular dissimilarity matrix - if cluster = FALSE), a list of integer vectors (the meta-communities - if cutat is not NULL), or an object of class hclust (if cluster is TRUE and cutat is NULL).

Details

Extracting meta-communities allows the user to explore community relatedness and structure at higher levels. Community relatedness is calculated using the Jaccard coefficient and the number of nodes that community i and j share: $$S(i,j)=\frac{|n_{i}\cap n_{j}|}{|n_{i}\cup n_{j}|}$$

References

Kalinka, A.T. and Tomancak, P. (2011). linkcomm: an R package for the generation, visualization, and analysis of link communities in networks of arbitrary size and type. Bioinformatics 27, 2011-2012.

See Also

meta.communities, cutDendrogramAt, hclust

Examples

# NOT RUN {
## Generate graph and extract link communities.
g <- swiss[,3:4]
lc <- getLinkCommunities(g)

## Cluster the link communities.
getClusterRelatedness(lc)

## Cluster the link communities, cut the dendrogram, and return the meta-communities.
getClusterRelatedness(lc, cutat = 1)
# }