make_Systematic {netcom}

Systematically Make Networks

Description

Creates a list of networks that systematically spans mechanisms and their respective parameters.

Usage

make_Systematic(
  net_size,
  neighborhood,
  directed = TRUE,
  net_kind = "matrix",
  mechanism_kind = "canonical",
  resolution = 100,
  resolution_min = 0.01,
  resolution_max = 0.99,
  reps = 3,
  processes = c("ER", "PA", "DM", "SW", "NM"),
  power_max = 5,
  connectance_max = 0.5,
  divergence_max = 0.5,
  mutation_max = 0.5,
  canonical = FALSE,
  cores = 1,
  verbose = TRUE
)

Arguments

net_size

Number of nodes in the network.

neighborhood

The range of nodes that form connected communities. Note: This implementation results in overlap of communities.

directed

Whether the target network is directed. Defaults to TRUE.

net_kind

If the network is an adjacency matrix ("matrix") or an edge list ("list"). Defaults to "matrix".

mechanism_kind

Either "canonical" or "grow" can be used to simulate networks. If "grow" is used, note that here it will only simulate pure mixtures made of a single mechanism. Defaults to "canonical".

resolution

The first step is to find the version of each process most similar to the target network. This parameter sets the number of parameter values to search across. Decrease to improve performance, but at the cost of accuracy. Defaults to 100.

resolution_min

= The minimum parameter value to consider. Zero is not used because in many processes it results in degenerate systems (e.g. entirely unconnected networks). Currently process agnostic. Future versions will accept a vector of values, one for each process. Defaults to 0.01.

resolution_max

The maximum parameter value to consider. One is not used because in many processes it results in degenerate systems (e.g. entirely connected networks). Currently process agnostic. Future versions will accept a vector of values, one for each process. Defaults to 0.99.

reps

Defaults to 3. The number of networks to simulate for each parameter. More replicates increases accuracy by making the estimation of the parameter that produces networks most similar to the target network less idiosyncratic.

processes

Defaults to c("ER", "PA", "DD", "SW", "NM"). Vector of process abbreviations. Currently only the default five are supported. Future versions will accept user-defined network-generating functions and associated parameters. ER = Erdos-Renyi random. PA = Preferential Attachment. DD = Duplication and Divergence. SW = Small World. NM = Niche Model.

power_max

Defaults to 5. The maximum power of attachment in the Preferential Attachment process (PA).

connectance_max

Defaults to 0.5. The maximum connectance parameter for the Niche Model.

divergence_max

Defaults to 0.5. The maximum divergence parameter for the Duplication and Divergence/Mutation mechanisms.

mutation_max

Defaults to 0.5. The maximum mutation parameter for the Duplication and Mutation mechanism.

canonical

Defautls to FALSE. If TRUE the mechanisms are directed or undirected in accordance with their canonical forms. This negates the value of 'directed'.

cores

Defaults to 1. The number of cores to run the classification on. When set to 1 parallelization will be ignored.

verbose

Defaults to TRUE. Whether to print all messages.

Details

Produces ground-truthing network data.

Value

A list. The first element contains the networks. The second contains their corresponding parameters.

References

Langendorf, R. E., & Burgess, M. G. (2020). Empirically Classifying Network Mechanisms. arXiv preprint arXiv:2012.15863.

Examples

# Import netcom
library(netcom)

make_Systematic(net_size = 10)