computeBCRedund: Compute Burchard and Cornwell's (2018) Two-Mode Redundancy
In dream: Dynamic Relational Event Analysis and Modeling

computeBCRedund

R Documentation

Compute Burchard and Cornwell's (2018) Two-Mode Redundancy

Description

This function calculates the values for two mode redundancy for weighted and unweighted two-mode networks based on Burchard and Cornwell (2018).

Usage

computeBCRedund(
  net,
  inParallel = FALSE,
  nCores = NULL,
  isolates = NA,
  weighted = FALSE
)

Arguments

`net`	A two-mode adjacency matrix or affiliation matrix.
`inParallel`	TRUE/FALSE. TRUE indicates that parallel processing will be used to compute the statistic with the foreach package. FALSE indicates that parallel processing will not be used. Set to FALSE by default.
`nCores`	If inParallel = TRUE, the number of computing cores for parallel processing. If this value is not specified, then the function internally provides it by dividing the number of available cores in half.
`isolates`	What value should isolates be given? Preset to be NA.
`weighted`	TRUE/FALSE. TRUE indicates the statistic will be based on the weighted formula (see the details section). FALSE indicates the statistic will be based on the original non-weighted formula. Set to FALSE by default.

Details

The formula for two-mode redundancy is:

r_{ij} = \frac{|\sigma(j) \cap \sigma(i)|}{|\sigma(i)|}

where:

r_{ij} is the redundancy of ego i with respect to actor j.
|\sigma(j) \cap \sigma(i)| is the number of same-class contacts (e.g., medical doctors in a hospital) that i and j both share.
|\sigma(i)| is the number of same-class contacts of ego i.

The two-mode redundancy is ego-bound, that is, the redundancy is only based on the two-mode ego network of i. Put differently, r_{ij} only considers the perspective of the ego. This function allows the user to compute the scores in parallel through the foreach and doParallel R packages. If the matrix is weighted, the user should specify weighted = TRUE. Following Burchard and Cornwell (2018), the formula for two-mode weighted redundancy is:

r_{ij} = \frac{|\sigma(j) \cap \sigma(i)|}{|\sigma(i)| \times w_t}

where w_t is the average of the tie weights that i and j send to their shared opposite class contacts.

Value

An n x n matrix with level 1 redundancy scores for actors in a two-mode network.

Author(s)

Kevin A. Carson kacarson@arizona.edu, Diego F. Leal dflc@arizona.edu

References

Burchard, Jake and Benjamin Cornwell. 2018. "Structural Holes and bridging in two-mode networks." Social Networks 55:11-20.

Examples


# For this example, we recreate Figure 2 in Burchard and Cornwell (2018: 13)
BCNet <- matrix(
 c(1,1,0,0,
   1,0,1,0,
   1,0,0,1,
   0,1,1,1),
 nrow = 4, ncol = 4, byrow = TRUE)
colnames(BCNet) <- c("1", "2", "3", "4")
rownames(BCNet) <- c("i", "j", "k", "m")
#library(sna) #To plot the two mode network, we use the sna R package
#gplot(BCNet, usearrows = FALSE,
#      gmode = "twomode", displaylabels = TRUE)
#this values replicate those reported by Burchard and Cornwell (2018: 14)
computeBCRedund(BCNet)


#For this example, we recreate Figure 9 in Burchard and Cornwell (2018:18)
#for weighted two mode networks.
BCweighted <- matrix(c(1,2,1, 1,0,0,
                      0,2,1,0,0,1),
                      nrow = 4, ncol = 3,
                      byrow = TRUE)
rownames(BCweighted) <- c("i", "j", "k", "l")
computeBCRedund(BCweighted, weighted = TRUE)

dream documentation built on Aug. 8, 2025, 6:36 p.m.