stresscent | R Documentation |
stresscent
takes one or more graphs (dat
) and returns the stress centralities of positions (selected by nodes
) within the graphs indicated by g
. Depending on the specified mode, stress 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).
stresscent(dat, g=1, nodes=NULL, gmode="digraph",
diag=FALSE, tmaxdev=FALSE, cmode="directed",
geodist.precomp=NULL, rescale=FALSE, ignore.eval=TRUE)
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. |
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 betweenness 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 density? |
The stress of a vertex, v, is given by
C_S(v) = \sum_{i,j : i \neq j,i \neq v,j \neq v} g_{ivj}
where g_{ijk}
is the number of geodesics from i to k through j. Conceptually, high-stress vertices lie on a large number of shortest paths between other vertices; they can thus be thought of as “bridges” or “boundary spanners.” Compare this with betweenness
, which weights shortest paths by the inverse of their redundancy.
A vector, matrix, or list containing the centrality scores (depending on the number and size of the input graphs).
Judicious use of geodist.precomp
can save a great deal of time when computing multiple path-based indices on the same network.
Carter T. Butts buttsc@uci.edu
Shimbel, A. (1953). “Structural Parameters of Communication Networks.” Bulletin of Mathematical Biophysics, 15:501-507.
centralization
g<-rgraph(10) #Draw a random graph with 10 members
stresscent(g) #Compute stress scores
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