ham_distance: Hamiltonian distance matrix creation

In CommKern: Network-Based Communities and Kernel Machine Methods

Hamiltonian distance matrix creation

Description

Description of the Hamiltonian distance matrix creation function.

Usage

ham_distance(hamil_df)

Arguments

hamil_df

a data frame containing two columns, one for network ID and another containing Hamiltonian values

Details

This function creates a distance matrix using the Hamiltonian output values from a community detection algorithm that implements a Hamiltonian value, such as the hierarchical multimodal spinglass algorithm. To ensure a positive, semi-definite matrix (as required for the kernel function), the absolute difference between Hamiltonian values is calculated.

The function returns an m x m matrix (where m is the number of networks) to be used as input for the kernel function.

Value

the Hamiltonian distance matrix to be used as input for the kernel function

See Also

hms

Examples

hamil_df <- data.frame(id  = seq(1:8),
                       ham = c(-160.5375, -167.8426, -121.7128, -155.7245,
                               -113.9834, -112.5262, -117.9724, -171.374))

ham_distance(hamil_df)

CommKern documentation built on Sept. 23, 2022, 5:07 p.m.