graphcent {sna} | R Documentation |
Compute the (Harary) Graph Centrality Scores of Network Positions
Description
graphcent
takes one or more graphs (dat
) and returns the Harary graph centralities of positions (selected by nodes
) within the graphs indicated by g
. Depending on the specified mode, graph centrality on directed or undirected geodesics 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).
Usage
graphcent(dat, g=1, nodes=NULL, gmode="digraph", diag=FALSE,
tmaxdev=FALSE, cmode="directed", geodist.precomp=NULL,
rescale=FALSE, ignore.eval)
Arguments
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 |
list 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. "digraph" indicates that edges should be interpreted as directed; "graph" indicates that edges are undirected. |
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 graph centrality being computed (directed or undirected geodesics). |
geodist.precomp |
a |
rescale |
if true, centrality scores are rescaled such that they sum to 1. |
ignore.eval |
logical; should edge values be ignored when calculating geodesics? |
Details
The Harary graph centrality of a vertex v is equal to \frac{1}{\max_u d(v,u)}
, where d(v,u)
is the geodesic distance from v to u. Vertices with low graph centrality scores are likely to be near the “edge” of a graph, while those with high scores are likely to be near the “middle.” Compare this with closeness
, which is based on the reciprocal of the sum of distances to all other vertices (rather than simply the maximum).
Value
A vector, matrix, or list containing the centrality scores (depending on the number and size of the input graphs).
Note
Judicious use of geodist.precomp
can save a great deal of time when computing multiple path-based indices on the same network.
Author(s)
Carter T. Butts buttsc@uci.edu
References
Hage, P. and Harary, F. (1995). “Eccentricity and Centrality in Networks.” Social Networks, 17:57-63.
See Also
Examples
g<-rgraph(10) #Draw a random graph with 10 members
graphcent(g) #Compute centrality scores