multilevel_degree: Degree centrality for multilevel networks

In anespinosa/netmem: Social Network Measures using Matrices

Degree centrality for multilevel networks

Description

Degree centrality for multilevel networks

Usage

multilevel_degree(
  A1,
  B1,
  A2 = NULL,
  B2 = NULL,
  A3 = NULL,
  B3 = NULL,
  complete = FALSE,
  digraphA1 = FALSE,
  digraphA2 = FALSE,
  digraphA3 = FALSE,
  typeA1 = "out",
  typeA2 = "out",
  typeA3 = "out",
  loopsA1 = FALSE,
  loopsA2 = FALSE,
  loopsA3 = FALSE,
  normalized = FALSE,
  weightedA1 = FALSE,
  weightedA2 = FALSE,
  weightedA3 = FALSE,
  alphaA1 = 0.5,
  alphaA2 = 0.5,
  alphaA3 = 0.5
)

Arguments

A1	The square matrix of the lowest level
B1	The incidence matrix of the ties between the nodes of first level and the nodes of the second level
A2	The square matrix of the second level
B2	The incidence matrix of the ties between the nodes of the second level and the nodes of the third level
A3	The square matrix of the third level
B3	The incidence matrix of the ties between the nodes of the third level and the nodes of the first level
complete	Add the degree of bipartite and tripartite networks for B1, B2 and/or B3, and the low_multilevel (i.e. A1+B1+B2+B3), meso_multilevel (i.e. B1+A2+B2+B3) and high_multilevel (i.e. B1+B2+A3+B3) degree
digraphA1	Whether A1 is a directed network
digraphA2	Whether A2 is a directed network
digraphA3	Whether A3 is a directed network
typeA1	Type of degree of the network for A1, "out" for out-degree, "in" for in-degree or "all" for the sum of the two
typeA2	Type of degree of the network for A2, "out" for out-degree, "in" for in-degree or "all" for the sum of the two
typeA3	Type of degree of the network for A3, "out" for out-degree, "in" for in-degree or "all" for the sum of the two
loopsA1	Whether the loops of the edges are considered in matrix A1
loopsA2	Whether the loops of the edges are considered in matrix A2
loopsA3	Whether the loops of the edges are considered in matrix A3
normalized	If TRUE then the result is divided by (n-1)+k+m for the first level, (m-1)+n+k for the second level, and (k-1)+m+n according to Espinosa-Rada et al. (2021)
weightedA1	Whether A1 is weighted
weightedA2	Whether A2 is weighted
weightedA3	Whether A3 is weighted
alphaA1	The alpha parameter of A1 according to Opsahl et al (2010) for weighted networks. The value 0.5 is given by default.
alphaA2	The alpha parameter of A2 according to Opsahl et al (2010) for weighted networks. The value 0.5 is given by default.
alphaA3	The alpha parameter of A3 according to Opsahl et al (2010) for weighted networks. The value 0.5 is given by default.

Value

Return a data.frame of multilevel degree

Author(s)

Alejandro Espinosa-Rada

References

Borgatti, S. P., and Everett, M. G. (1997). Network analysis of 2-mode data. Social Networks, 19(3), 243–269.

Freeman, L. C. (1978). Centrality in social networks conceptual clarification. Social Networks, 1(3), 215–239.

Opsahl, T., Agneessens, F., and Skvoretz, J. (2010). Node centrality in weighted networks: Generalizing degree and shortest paths. Social Networks, 32(3), 245–251.

Examples

A1 <- matrix(c(
  0, 1, 0, 0, 0,
  1, 0, 0, 1, 0,
  0, 0, 0, 1, 0,
  0, 1, 1, 0, 1,
  0, 0, 0, 1, 0
), byrow = TRUE, ncol = 5)

B1 <- matrix(c(
  1, 0, 0,
  1, 1, 0,
  0, 1, 0,
  0, 1, 0,
  0, 1, 1
), byrow = TRUE, ncol = 3)

A2 <- matrix(c(
  0, 1, 1,
  1, 0, 0,
  1, 0, 0
), byrow = TRUE, nrow = 3)

B2 <- matrix(c(
  1, 1, 0, 0,
  0, 0, 1, 0,
  0, 0, 1, 1
), byrow = TRUE, ncol = 4)

A3 <- matrix(c(
  0, 1, 1, 1,
  1, 0, 0, 0,
  1, 0, 0, 1,
  1, 0, 1, 0
), byrow = TRUE, ncol = 4)

B3 <- matrix(c(
  1, 0, 0, 0, 0,
  0, 1, 0, 1, 0,
  0, 0, 0, 0, 0,
  0, 0, 0, 0, 0
), byrow = TRUE, ncol = 5)

multilevel_degree(A1, B1, A2, B2, A3, B3)
## Not run: 
multilevel_degree(A1, B1, A2, B2, A3, B3, normalized = TRUE, complete = TRUE)

## End(Not run)

anespinosa/netmem documentation built on April 5, 2025, 5:02 p.m.