ext_distance {CommKern}

Extrinsic evaluation distance matrix creation

Description

Description of the extrinsic evaluation distance matrix creation function.

Usage

ext_distance(comm_df, variant = c("NMI", "adj_RI", "purity"))

Arguments

comm_df

a data frame whose columns are different partitions. All partitions must have the same set of nodes in order for this function to work and this data frame should exclude a node ID column for ease of computation.

variant

a string in ('NMI', 'Adj_RI', 'purity') that calculates different extrinsic cluster evaluation metrics.

Details

This function creates a distance matrix using the community output values from any community detection algorithm, such as the hierarchical multimodal spinglass algorithm. Because extrinsic evaluation metrics for clustering algorithms use the underlying idea of similarity, distance is calculated as (1-similarity). The use of distance ensures that the distance matrix will be positive and semi-definite, a requirement for its use in the kernel function.

Value

A m x m (m is the number of partitions) extrinsic evaluation distance matrix to be used as input for the kernel function

See Also

adj_RI, NMI, and purity

Examples

x <- c(2,2,3,1,3,1,3,3,2,2,1,1)
y <- c(3,3,2,1,1,1,1,2,2,3,2,3)
z <- c(1,1,2,3,2,3,2,1,1,2,3,3)

xyz_comms <- data.frame(x_comm = x, y_comm = y, z_comm = z)
ext_distance(xyz_comms, variant = 'NMI')
ext_distance(xyz_comms, variant = 'adj_RI')
ext_distance(xyz_comms, variant = 'purity')