degree_centrality | R Documentation |
These functions calculate common centrality measures for one- and two-mode networks.
All measures attempt to use as much information as they are offered,
including whether the networks are directed, weighted, or multimodal.
If this would produce unintended results,
first transform the salient properties using e.g. to_undirected()
functions.
All centrality and centralization measures return normalized measures by default,
including for two-mode networks.
node_degree(
.data,
normalized = TRUE,
alpha = 0,
direction = c("all", "out", "in")
)
node_outdegree(.data, normalized = TRUE, alpha = 0)
node_indegree(.data, normalized = TRUE, alpha = 0)
tie_degree(.data, normalized = TRUE)
network_degree(.data, normalized = TRUE, direction = c("all", "out", "in"))
network_outdegree(.data, normalized = TRUE)
network_indegree(.data, normalized = TRUE)
.data |
An object of a
|
normalized |
Logical scalar, whether the centrality scores are normalized. Different denominators are used depending on whether the object is one-mode or two-mode, the type of centrality, and other arguments. |
alpha |
Numeric scalar, the positive tuning parameter introduced in
Opsahl et al (2010) for trading off between degree and strength centrality measures.
By default, |
direction |
Character string, “out” bases the measure on outgoing ties, “in” on incoming ties, and "all" on either/the sum of the two. For two-mode networks, "all" uses as numerator the sum of differences between the maximum centrality score for the mode against all other centrality scores in the network, whereas "in" uses as numerator the sum of differences between the maximum centrality score for the mode against only the centrality scores of the other nodes in that mode. |
A single centralization score if the object was one-mode, and two centralization scores if the object was two-mode.
Depending on how and what kind of an object is passed to the function,
the function will return a tidygraph
object where the nodes have been updated
node_degree()
: Calculates the degree centrality of nodes in an unweighted network,
or weighted degree/strength of nodes in a weighted network.
node_outdegree()
: Wraps node_degree(..., direction = "out")
node_indegree()
: Wraps node_degree(..., direction = "in")
tie_degree()
: Calculate the degree centrality of edges in a network
network_degree()
: Calculate the degree centralization for a graph
network_outdegree()
: Wraps network_degree(..., direction = "out")
network_indegree()
: Wraps network_degree(..., direction = "out")
Faust, Katherine. 1997. "Centrality in affiliation networks." Social Networks 19(2): 157-191. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/S0378-8733(96)00300-0")}.
Borgatti, Stephen P., and Martin G. Everett. 1997. "Network analysis of 2-mode data." Social Networks 19(3): 243-270. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/S0378-8733(96)00301-2")}.
Borgatti, Stephen P., and Daniel S. Halgin. 2011. "Analyzing affiliation networks." In The SAGE Handbook of Social Network Analysis, edited by John Scott and Peter J. Carrington, 417–33. London, UK: Sage. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.4135/9781446294413.n28")}.
Opsahl, Tore, Filip Agneessens, and John Skvoretz. 2010. "Node centrality in weighted networks: Generalizing degree and shortest paths." Social Networks 32, 245-251. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.socnet.2010.03.006")}
to_undirected()
for removing edge directions
and to_unweighted()
for removing weights from a graph.
Other measures:
between_centrality
,
close_centrality
,
closure
,
cohesion()
,
eigenv_centrality
,
features
,
heterogeneity
,
hierarchy
,
holes
Other centrality:
between_centrality
,
close_centrality
,
eigenv_centrality
node_degree(mpn_elite_mex)
node_degree(ison_southern_women)
tie_degree(ison_adolescents)
network_degree(ison_southern_women, direction = "in")
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