nemBM (version 1.00.01)

nemSymBMinout: Network evolution model with a prespecified blockmodel type and partition (symmetric networks with incomers and outgoers)

Description

Generate a symmetric network with a selected blockmodel type and partition with a specified number of incomers and outgoers. Considers local network mechanisms when creating links within blocks.

Usage

nemSymBMinout(
  X = X,
  partition = partition,
  M = M,
  formula = NULL,
  theta = NULL,
  nin = 5,
  nout = 20,
  minClusterSize = 5,
  k = 1000,
  loops = FALSE,
  randomizeP = 0,
  randomSD = 0.02
)

Value

The list with the following elements:

• initialNetwork - Initial network; of class matrix.

• finalNetwork - Final (generated) network; of class matrix.

• initialPartition - Initial partition.

• finalPartition - Final partition (i.e., partition after randomization and after incomers and outgoers).

• M - The desired (specified) image matrix.

• k - The number of iterations.

• combinedPartitions - Data frame with initial and final partition.

• whenIncomers - A vector of which elements tells us at which iterations the incomers were added.

• whenOutgoers - A vector of which elements tells us at which iterations the outgoers were removed.

• ERR - Sum of squared differences between the desired and empirical densities across blocks; for each iteration.

• linkERR - The difference in the number of links between the generated number of links and desired number of links; for each iteration.

Arguments

X

Initial binary network; of class matrix.

partition

A desired partition in a vector format. Each unique value (positive integers) represents one cluster.

M

Desired image matrix with block densities.

formula

The list of local netork mechanisms to be considered.

theta

A vector with the mechanisms' weights/strengths.

nin

Number of incomers.

nout

Number of outgoers.

minClusterSize

Minimum cluster size.

k

Number of iterations.

loops

Wheter loops are allowed or not (default FALSE).

randomizeP

The share of units to be randomly relocated between clusters.

randomSD

The srandard deviation of a normal distribution form which the random part of weighed network statistics is sampled.

Author

Marjan Cugmas and Aleš Žiberna

Examples

formula <- list(mutuality, popularity, OTPtransitivity)
X <- matrix(sample(c(0,1), size = 9**2, replace = TRUE), nrow = 9)
diag(X) <- 0
M <- matrix(c(0.1, 0.8, 0.8, 0.1), nrow = 2)
partition <- c(1, 2, 2, 1, 1, 2, 2, 2, 1)
nemSymBMinout(X = X, 
              partition = partition, 
              formula = formula, 
              theta = c(1, 1, 1), 
              M = M, 
              k = 100, 
              minClusterSize = 2,
              nin = 10,
              nout = 5,
              loops = FALSE)