`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, `g==1`

.

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. `gmode`

is set to `"digraph"`

by default.

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. `diag`

is `FALSE`

by default.

tmaxdev

boolean indicating whether or not the theoretical maximum absolute deviation from the maximum nodal centrality should be returned. By default, `tmaxdev==FALSE`

.

cmode

string indicating the type of betweenness centrality being computed (directed or undirected geodesics).

geodist.precomp

a `geodist`

object precomputed for the graph to be analyzed (optional).

rescale

if true, centrality scores are rescaled such that they sum to 1.

ignore.eval

logical; should edge values be ignored when calculating density?

A vector, matrix, or list containing the centrality scores (depending on the number and size of the input graphs).

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.

Shimbel, A. (1953). ``Structural Parameters of Communication Networks.'' *Bulletin of Mathematical Biophysics,* 15:501-507.

```
# NOT RUN {
g<-rgraph(10) #Draw a random graph with 10 members
stresscent(g) #Compute stress scores
# }
```

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