attach_layout {metanetwork}

compute and attach metanetwork layouts

Description

Method to compute 'TL-tsne' and 'group-TL-tsne' layouts and save it as node attributes of the focal network.

Usage

attach_layout(
  metanetwork,
  g = NULL,
  beta = 0.1,
  mode = "TL-tsne",
  TL_tsne.config = TL_tsne.default,
  res = NULL,
  group_layout.config = group_layout.default
)

## S3 method for class 'metanetwork'
attach_layout(
  metanetwork,
  g = NULL,
  beta = 0.1,
  mode = "TL-tsne",
  TL_tsne.config = TL_tsne.default,
  res = NULL,
  group_layout.config = group_layout.default
)

Arguments

metanetwork

object of class 'metanetwork'

g

character indicating the name of the network for which the 'TL-tsne' layout is computed, default is 'metaweb'

beta

the diffusion parameter of the diffusion kernel, a positive scalar controlling the squeezing of the network, default is 0.1

mode

'TL-tsne' or 'group-TL-tsne', default is 'TL-tsne'.

TL_tsne.config

configuration list for mode 'TL-tsne', default is TL_tsne.default

res

resolution for the 'group-TL-tsne' layout

group_layout.config

configuration list for mode 'group-TL-tsne', default is group_layout.default

Details

The 'TL-tsne' layout is a diffusion based layout algorithm specifically designed for trophic networks. In metanetwork, first axis is the trophic level (see compute_TL method) whereas the second axis is computed using a diffusion graph kernel (Kondor & Lafferty 2002) and tsne dimension reduction algorithm to (see van der Maaten & Hinton (2008) and 'tsne' R package). Let A be the adjacency matrix of the considered network and D its degree diagonal matrix. The Laplacian matrix of the symmetrised network is defined by:

L = D - A - t(A)

The diffusion graph kernel is:

K = exp(-beta*L)

It is a similarity matrix between nodes according to a diffusion process. beta is the diffusion constant,it must be provided by the user. beta parameter influences the layout by grouping together similar paths (see pyramid vignette). Each node of the focal network has an attribute layout_beta_VALUE. If this function is run several times for a given beta value, repetitions of the layout algorithm will be stored as node attributes.

The 'group-TL-tsne' layout is a variation of ⁠'TL-tsne⁠ layout. For a focal network, it mixes 'TL-tsne' layout at the desired aggregated level with the layout_with_graphopt function from igraph. It clusters nodes belonging to the same group. 'group-TL-tsne' layout is recommended for large networks since you only need to compute 'TL-tsne' at the aggregated network that is much smaller than the focal network. group_layout.config allows controlling the overall size of the groups.

Value

an object of class 'metanetwork', with the computed layout added as node attribute of the considered network

NULL

References

Kondor, R. I., & Lafferty, J. (2002, July). Diffusion kernels on graphs and other discrete structures. In Proceedings of the 19th international conference on machine learning (Vol. 2002, pp. 315-322). Van der Maaten, L., & Hinton, G. (2008). Visualizing data using t-SNE. Journal of machine learning research, 9(11).

See Also

ggmetanet(), vismetaNetwork(),group_layout.default

Examples

library(metanetwork)
library(igraph)
# on angola dataset (metaweb)
data("meta_angola")
meta_angola = attach_layout(meta_angola,beta = 0.05)
V(meta_angola$metaweb)$layout_beta0.05