stresscent {sna} | R Documentation

## Compute the Stress Centrality Scores of Network Positions

### Description

`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).

### Usage

    stresscent(dat, g=1, nodes=c(1:dim(dat)[2]), gmode="digraph",
        diag=FALSE, tmaxdev=FALSE, cmode="directed",
        geodist.precomp=NULL, rescale=FALSE)

### 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, `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.

### Details

The stress of a vertex, v, is given by

C_S(v) = sum( g_ivj, i,j: i!=j,i!=v,j!=v)

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.

### 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

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

### See Also

`centralization`

### Examples

    g<-rgraph(10)     #Draw a random graph with 10 members
    stresscent(g)     #Compute stress scores

[Package sna version 1.1 Index]