best_fit_optim: Empirical parameterization

In langendorfr/netcom: NETwork COMparison Inference

Empirical parameterization

Description

Helper function to find the best fitting version of a mechanism by searching across its parameter space

Usage

best_fit_optim(
  parameter,
  process,
  network,
  net_size,
  net_kind,
  mechanism_kind,
  resolution,
  resolution_min,
  resolution_max,
  reps,
  power_max,
  connectance_max,
  divergence_max,
  mutation_max,
  cores,
  directed,
  method,
  cause_orientation,
  DD_kind,
  DD_weight,
  max_norm,
  best_fit_kind = "avg",
  verbose = FALSE
)

Arguments

parameter	The parameter being tested for its ability to generate networks alike the input 'network'.
process	Name of mechanism. Currently only "ER", "PA", "DD", "DM" "SW", and "NM" 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. DM = Duplication and Mutation. SW = Small World. NM = Niche Model.
network	The network being compared to a hypothesized 'process' with a given 'parameter' value.
net_size	Number of nodes in the network.
net_kind	If the network is an adjacency matrix ("matrix") or an edge list ("list").
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.
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.
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.
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.
reps	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.
power_max	The maximum power of attachment in the Preferential Attachment process (PA).
connectance_max	The maximum connectance parameter for the Niche Model.
divergence_max	The maximum divergence parameter for the Duplication and Divergence/Mutation mechanisms.
mutation_max	The maximum mutation parameter for the Duplication and Mutation mechanism.
cores	The number of cores to run the classification on. When set to 1 parallelization will be ignored.
directed	Whether the target network is directed.
method	This determines the method used to compare networks at the heart of the classification. Currently "DD" (Degree Distribution) and "align" (the align function which compares networks by the entropy of diffusion on them) are supported. Future versions will allow user-defined methods.
cause_orientation	The orientation of directed adjacency matrices.
DD_kind	A vector of network properties to be used to compare networks.
DD_weight	Weights of each network property in DD_kind. Defaults to 1, which is equal weighting for each property.
max_norm	Binary variable indicating if each network property should be normalized so its max value (if a node-level property) is one.
best_fit_kind	How to aggregate the stochastic replicates of the process + parameter combination.
verbose	Defaults to TRUE. Whether to print all messages.

Details

Note: Currently each process is assumed to have a single governing parameter.

Value

A number measuring how different the input network is from the parameter + process combination.

References

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

Examples

# Import netcom
library(netcom)

# Adjacency matrix
size <- 10
network <- matrix(sample(c(0,1), size = size^2, replace = TRUE), nrow = size, ncol = size)

# Calculate how similar the input network is to Small-World networks with 
# a rewiring probability of 0.28.
best_fit_optim(
     parameter = 0.28, 
     process = "SW", 
     network = network, 
     net_size = 12, 
     net_kind = "matrix", 
     mechanism_kind = "grow", 
     resolution = 100, 
     resolution_min = 0.01, 
     resolution_max = 0.99, 
     reps = 3, 
     power_max = 5, 
     connectance_max = 0.5, 
     divergence_max = 0.5, 
     mutation_max = 0.5, 
     cores = 1, 
     directed = TRUE, 
     method = "DD", 
     cause_orientation = "row", 
     DD_kind = c(
         "in", "out", "entropy_in", "entropy_out", 
         "clustering_coefficient", "page_rank", "communities"
     ), 
     DD_weight = 1, 
     max_norm = FALSE,
     verbose = FALSE
)

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