gen_degree: Generalized degree

In anespinosa/netmem: Social Network Measures using Matrices

Generalized degree

Description

Generalized degree centrality for one-mode and bipartite networks

Usage

gen_degree(
  A,
  weighted = FALSE,
  type = "out",
  normalized = FALSE,
  loops = TRUE,
  digraph = TRUE,
  alpha = 0.5,
  bipartite = FALSE
)

Arguments

A	A matrix object
weighted	Whether the matrix is weighted or not
type	Character string, “out” (outdegree), “in” (indegree) and “all” (degree)
normalized	Whether normalize the measure for the one-mode network (Freeman, 1978) or a bipartite network (Borgatti and Everett, 1997)
loops	Whether the diagonal of the matrix is considered or not
digraph	Whether the matrix is directed or undirected
alpha	Sets the alpha parameter in the generalised measures from Opsahl et al. (2010)
bipartite	Whether the matrix is bipartite or not.

Value

This function returns term 1, 2 and 3, the normalization and the maximum value of the specification of Everett and Borgatti (2020), and the constraint of Burt (1992)

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

A3 <- matrix(c(
  0, 4, 4, 0, 0, 0,
  4, 0, 2, 1, 1, 0,
  4, 2, 0, 0, 0, 0,
  0, 1, 0, 0, 0, 0,
  0, 1, 0, 0, 0, 7,
  0, 0, 0, 0, 7, 0
), byrow = TRUE, ncol = 6)

gen_degree(A3, digraph = FALSE, weighted = TRUE)

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