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)
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.