degree_centrality  R Documentation 
These functions calculate common degreerelated centrality measures for one and twomode networks:
node_degree()
measures the degree centrality of nodes in an unweighted network,
or weighted degree/strength of nodes in a weighted network;
there are several related shortcut functions:
node_deg()
returns the unnormalised results.
node_indegree()
returns the direction = 'in'
results.
node_outdegree()
returns the direction = 'out'
results.
node_multidegree()
measures the ratio between types of ties in a multiplex network.
node_posneg()
measures the PN (positivenegative) centrality of a signed network.
tie_degree()
measures the degree centrality of ties in a network
network_degree()
measures a network's degree centralization;
there are several related shortcut functions:
network_indegree()
returns the direction = 'out'
results.
network_outdegree()
returns the direction = 'out'
results.
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 twomode networks.
node_degree(
.data,
normalized = TRUE,
alpha = 1,
direction = c("all", "out", "in")
)
node_deg(.data, alpha = 0, direction = c("all", "out", "in"))
node_outdegree(.data, normalized = TRUE, alpha = 0)
node_indegree(.data, normalized = TRUE, alpha = 0)
node_multidegree(.data, tie1, tie2)
node_posneg(.data)
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 onemode or twomode, 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 twomode 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. 
tie1 
Character string indicating the first uniplex network. 
tie2 
Character string indicating the second uniplex network. 
A single centralization score if the object was onemode, and two centralization scores if the object was twomode.
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
Faust, Katherine. 1997. "Centrality in affiliation networks." Social Networks 19(2): 157191. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/S03788733(96)003000")}.
Borgatti, Stephen P., and Martin G. Everett. 1997. "Network analysis of 2mode data." Social Networks 19(3): 243270. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/S03788733(96)003012")}.
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, 245251. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.socnet.2010.03.006")}
Everett, Martin G., and Stephen P. Borgatti. 2014. “Networks Containing Negative Ties.” Social Networks 38:111–20. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.socnet.2014.03.005")}.
to_undirected()
for removing edge directions
and to_unweighted()
for removing weights from a graph.
Other centrality:
between_centrality
,
close_centrality
,
eigenv_centrality
Other measures:
between_centrality
,
close_centrality
,
closure
,
cohesion()
,
eigenv_centrality
,
features
,
heterogeneity
,
hierarchy
,
holes
,
net_diffusion
,
node_diffusion
,
periods
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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