make_Systematic_mixture: Systematically Make Networks

In langendorfr/netcom: NETwork COMparison Inference

Systematically Make Networks

Description

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

Usage

make_Systematic_mixture(
  net_size,
  neighborhood,
  directed = TRUE,
  net_kind = "matrix",
  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".
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)

langendorfr/netcom documentation built on July 23, 2022, 5:19 p.m.