sna: sedist – R documentation

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sedist

Find a Matrix of Distances Between Positions Based on Structural Equivalence

Description

sedist uses the graphs indicated by g in dat to assess the extent to which each vertex is structurally equivalent; joint.analysis determines whether this analysis is simultaneous, and method determines the measure of approximate equivalence which is used.

Usage

sedist(dat, g=c(1:dim(dat)[1]), method="hamming", 
    joint.analysis=FALSE, mode="digraph", diag=FALSE, code.diss=FALSE)

Arguments

`dat`	a graph or set thereof.
`g`	a vector indicating which elements of `dat` should be examined.
`method`	one of `"correlation"`, `"euclidean"`, `"hamming"`, or `"gamma"`.
`joint.analysis`	should equivalence be assessed across all networks jointly (`TRUE`), or individually within each (`FALSE`)?
`mode`	`"digraph"` for directed data, otherwise `"graph"`.
`diag`	boolean indicating whether diagonal entries (loops) should be treated as meaningful data.
`code.diss`	reverse-code the raw comparison values.

Details

sedist provides a basic tool for assessing the (approximate) structural equivalence of actors. (Two vertices i and j are said to be structurally equivalent if i->k iff j->k for all k.) SE similarity/difference scores are computed by comparing vertex rows and columns using the measure indicated by method:

correlation: the product-moment correlation
euclidean: the euclidean distance
hamming: the Hamming distance
gamma: the gamma correlation

Once these similarities/differences are calculated, the results can be used with a clustering routine (such as equiv.clust) or an MDS (such as cmdscale).

Value

A matrix of similarity/difference scores

Note

Be careful to verify that you have computed what you meant to compute, with respect to similarities/differences. Also, note that (despite its popularity) the product-moment correlation can give rather strange results in some cases.

Author(s)

Carter T. Butts buttsc@uci.edu

References

Breiger, R.L.; Boorman, S.A.; and Arabie, P. (1975). “An Algorithm for Clustering Relational Data with Applications to Social Network Analysis and Comparison with Multidimensional Scaling.” Journal of Mathematical Psychology, 12, 328-383.

Burt, R.S. (1976). “Positions in Networks.” Social Forces, 55, 93-122.

Wasserman, S., and Faust, K. Social Network Analysis: Methods and Applications. Cambridge: Cambridge University Press.

Examples

#Create a random graph with _some_ edge structure
g.p<-sapply(runif(20,0,1),rep,20)  #Create a matrix of edge 
                                   #probabilities
g<-rgraph(20,tprob=g.p)            #Draw from a Bernoulli graph 
                                   #distribution

#Get SE distances
g.se<-sedist(g)

#Plot a metric MDS of vertex positions in two dimensions
plot(cmdscale(as.dist(g.se)))

sna

Tools for Social Network Analysis

v2.6

GPL (>= 2)

Authors

Carter T. Butts [aut, cre, cph]

Initial release

2020-10-5