fcd: Fused community detection.

In fcd: Fused Community Detection

Description

Get the fused community detection path object.

Usage

fcd(A, K = 2, nlambda = 1e+3, lambda.min.ratio = 1e-05, alpha = 0.8, scale = FALSE)
fcd.start(A, K = 2, nlambda = 1000, lambda.min.ratio = 1e-05, alpha = 0.8, scale = FALSE)

Arguments

A	input matrix – adjacency matrix of an observed graph based on the non-isolated nodes, of dimension n.noniso x n.noniso, where n.noniso is the number of the non-isolated nodes.
K	input integer – the pre-specified number of communities, with the default value 2.
nlambda	The number of lambda values - default is 1000.
lambda.min.ratio	Smallest value for lambda, as a fraction of lambda.max, the (data derived) entry value (i.e. the smallest value for which all coefficients are zero) - default is 1e-05.
alpha	The elasticnet mixing parameter - default is 0.8.
scale	The logic indicator of whether scaling the resulting matrix - default is FALSE.

Value

beta.combind	the estimators along the path.
iso.seq	a vector of indices of the isolated nodes.
cluster.list	list of clustering results.
criteria.list	list of criteria values.
final.ratio.cluster	the final estimator of the community labels according to the ratio cut criterion.
ratio.location	the location of the chosen estimator on the path according to the ratio cut criterion.
final.normalised.cluster	the final estimator of the community labels according to the normalised cut criterion.
normalised.location	the location of the chosen estimator on the path according to the normalised cut criterion.
lambda.list	the lambda sequence used for the path.

Author(s)

Yang Feng, Richard J. Samworth and Yi Yu

References

Feng, Y., Samworth, R. J. and Yu, Y., Fused Community Detection, manuscript.

fcd documentation built on May 2, 2019, 3:28 p.m.