Compute the Degree Centrality Scores of Network Positions
Degree takes one or more graphs (dat) and returns the degree centralities of positions (selected by nodes) within the graphs indicated by g. Depending on the specified mode, indegree, outdegree, or total (Freeman) degree will be returned; this function is compatible with centralization, and will return the theoretical maximum absolute deviation (from maximum) conditional on size (which is used by centralization to normalize the observed centralization score).
degree(dat, g=1, nodes=NULL, gmode="digraph", diag=FALSE,
tmaxdev=FALSE, cmode="freeman", rescale=FALSE, ignore.eval=FALSE)dat |
one or more input graphs. |
g |
integer indicating the index of the graph for which centralities are to be calculated (or a vector thereof). By default, |
nodes |
vector indicating which nodes are to be included in the calculation. By default, all nodes are included. |
gmode |
string indicating the type of graph being evaluated. |
diag |
boolean indicating whether or not the diagonal should be treated as valid data. Set this true if and only if the data can contain loops. |
tmaxdev |
boolean indicating whether or not the theoretical maximum absolute deviation from the maximum nodal centrality should be returned. By default, |
cmode |
string indicating the type of degree centrality being computed. |
rescale |
if true, centrality scores are rescaled such that they sum to 1. |
ignore.eval |
logical; should edge values be ignored when computing degree scores? |
Degree centrality is the social networker's term for various permutations of the graph theoretic notion of vertex degree: for unvalued graphs, indegree of a vertex, v, corresponds to the cardinality of the vertex set N^+(v) = {i in V(G) : (i,v) in E(G)}; outdegree corresponds to the cardinality of the vertex set N^-(v) = {i in V(G) : (v,i) in E(G)}; and total (or “Freeman”) degree corresponds to |N^+(v)|+|N^-(v)|. (Note that, for simple graphs, indegree=outdegree=total degree/2.) Obviously, degree centrality can be interpreted in terms of the sizes of actors' neighborhoods within the larger structure. See the references below for more details.
When ignore.eval==FALSE, degree weights edges by their values where supplied. ignore.eval==TRUE ensures an unweighted degree score (independent of input). Setting gmode=="graph" forces behavior equivalent to cmode=="indegree" (i.e., each edge is counted only once); to obtain a total degree score for an undirected graph in which both in- and out-neighborhoods are counted separately, simply use gmode=="digraph".
A vector, matrix, or list containing the degree scores (depending on the number and size of the input graphs).
Carter T. Butts buttsc@uci.edu
Freeman, L.C. (1979). “Centrality in Social Networks I: Conceptual Clarification.” Social Networks, 1, 215-239.
#Create a random directed graph dat<-rgraph(10) #Find the indegrees, outdegrees, and total degrees degree(dat,cmode="indegree") degree(dat,cmode="outdegree") degree(dat)
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