fpca.cut: The ratio cut and normalised cut values along the path

Description Usage Arguments Value Author(s) References See Also Examples

Description

Get the ratio cut and normalised cut values for the estimators along the path. It is part of the function in fpca, the main part of this function is single.cut.

Usage

1
fpca.cut(A, obj, fpca.cluster, K = 2, iso.seq)

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.

obj

a fpca.start object, which is a list containing iso.seq. This argument is used only if iso.seq is missing.

fpca.cluster

a list of vectors, with each vector as the estimator of the community labels of the non-isolated nodes in the network, of dimension n.noniso, values taken from 1 to K, where K is the number of communities.

K

the number of the communities, with 2 as the default value.

iso.seq

a vector of the indices of those isolated nodes in the graph. If it is missing, obj should be offered.

Value

ratio.list

a list of ratio cut values for the estimator path.

normalised.list

a list of normalised cut values for the estimator path.

Author(s)

Yang Feng, Richard J. Samworth and Yi Yu

References

Yang Feng, Richard J. Samworth and Yi Yu, Community Detection via Fused Principal Component Analysis, manuscript. Holland, P.W., Laskey, K.B. and Leinhardt, S., 1983. Stochastic block models: first steps. Social Networks 5, 109-137. Jin, J., 2012. Fast community detection by score. Karrer, B. and Newman, M.E.J., 2011. Stochastic blockmodels and community structure in networks. Physical Review E 83, 016107.

See Also

single.cut, fpca, fpca.start.

Examples

1
### please see the examples in fpca


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