# The ratio cut and normalised cut values along the path

### 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 |

`obj` |
a |

`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 |

`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, |

### 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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