randomize_asymmetric_hierarchical: Maximise pairwise and community asymmetry

In franzikoch/Feedbackloops: Feedback loop analysis for competition networks

Maximise pairwise and community asymmetry

Description

During this randomisation procedure, all interaction strengths are reordered to make the 2-link loops in the randomised system as asymmetric as possible. To do this, all links are ordered by size. Then, the very strongest link is paired with the weakest one, the second strongest with the second weakest etc. The location of pairwise interactions in the network is chosen randomly (but network topology is preserved- off-diagonal zeros remain in place).

Usage

randomize_asymmetric_hierarchical(it, ij_col, ji_col)

Arguments

it	interaction table (created by interaction_strengths())
ij_col	column of a_ij values to randomise (choose scaled or unscaled)
ji_col	column of a_ji values to randomise (scaled or unscaled)

Details

To also maximise community asymmetry, the stronger link of each interaction is placed below the diagonal of the matrix (aji column), while all weaker links are placed above the diagonal (aji column).

The function returns a new interaction table that contains two new columns $a_ij_asym_h and $a_ji_asym_h that contains the same values as the original columns but in a different order.

To get a Jacobian matrix with maximised pairwise asymmetry, use assemble_jacobian() and specify the new columns.

Value

Interaction table with additional columns $a_ij_asym_h and $a_ji_asym_h, containing randomised interaction strengths

franzikoch/Feedbackloops documentation built on July 1, 2023, 12:42 p.m.