| prestige {sna} | R Documentation |
prestige takes a graph stack (dat) and returns the prestige scores of positions within one graph (indicated by nodes and g, respectively). Depending on the specified mode, prestige based on any one of a number of different definitions 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).
prestige(dat, g=1, nodes=c(1:dim(dat)[2]), gmode="digraph",
diag=FALSE, cmode="indegree", tmaxdev=FALSE, rescale=FALSE,
tol=1e-07)
dat |
Data array to be analyzed. By assumption, the first dimension of the array indexes the graph, with the next two indexing the actors. Alternately, this can be an n x n matrix (if only one graph is involved). |
g |
Integer indicating the index of the graph for which centralities are to be calculated. 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. |
cmode |
One of "indegree", "indegree.rownorm", "indegree.rowcolnorm", "eigenvector", "eigenvector.rownorm", "eigenvector.colnorm", "eigenvector.rowcolnorm", "domain", or "domain.proximity" |
tmaxdev |
Boolean indicating whether or not the theoretical maximum absolute deviation from the maximum nodal centrality should be returned. By default, tmaxdev==FALSE. |
rescale |
If true, centrality scores are rescaled such that they sum to 1. |
tol |
Currently ignored |
"Prestige" is the name collectively given to a range of centrality scores which focus on the extent to which one is nominated by others. The definitions supported here are as follows:
Note that the centralization of prestige is simply the extent to which one actor has substantially greater prestige than others; the underlying definition is the same.
A vector of prestige scores
Making adjacency matrices doubly stochastic (row-column normalization) is not guaranteed to work. In general, be wary of attempting to try normalizations on graphs with degenerate rows and columns.
Carter T. Butts ctb@andrew.cmu.edu
Lin, N. (1976). Foundations of Social Research. New York: McGraw Hill.
Wasserman, S., and Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge: Cambridge University Press.
g<-rgraph(10) #Draw a random graph with 10 members prestige(g,cmode="domain") #Compute domain prestige scores