Returns the central graph of a set of labeled graphs, i.e. that graph in which i->j iff i->j in >=50% of the graphs within the set. If
normalize==TRUE, then the value of the i,jth edge is given as the proportion of graphs in which i->j.
one or more input graphs.
boolean indicating whether the results should be normalized. The result of this is the "mean matrix". By default,
The central graph of a set of graphs S is that graph C which minimizes the sum of Hamming distances between C and G in S. As such, it turns out (for the dichotomous case, at least), to be analogous to both the mean and median for sets of graphs. The central graph is useful in a variety of contexts; see the references below for more details.
A matrix containing the central graph (or mean matrix)
0.5 is used as the cutoff value regardless of whether or not the data is dichotomous (as is tacitly assumed). The routine is unaffected by data type when
Carter T. Butts firstname.lastname@example.org
Banks, D.L., and Carley, K.M. (1994). “Metric Inference for Social Networks.” Journal of Classification, 11(1), 121-49.
#Generate some random graphs dat<-rgraph(10,5) #Find the central graph cg<-centralgraph(dat) #Plot the central graph gplot(cg) #Now, look at the mean matrix cg<-centralgraph(dat,normalize=TRUE) print(cg)
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