For a single pair of nodes, implement the RSS algorithm of Chen et al. (2012).
1 2 3  RelationStrengthSimilarity(xadj, v1, v2, radius = 3,
directed = TRUE,
method = c("Rcpp", "BetterR", "NaiveR"))

xadj 
numeric matrix, then description of

v1 
numeric Object type, then description of

v2 
numeric Object type, then description of

radius 
numeric, length of longest path examined
from 
directed 
logical, if TRUE returns a symmetric RSS matrix. 
method 
character, choose the method of calculation. 
If v1
and v2
are specified, this returns
the RSS from v1
to v2
. If not, it
calculates the RSS scores for all dyads in the network.
numeric, Relation Strength Similarity score(s).
Stephen R. Haptonstahl srh@haptonstahl.org
"Discovering Missing Links in Networks Using Similarity Measures", HungHsuan Chen, Liang Gou, Xiaolong (Luke) Zhang, C. Lee Giles. 2012.
https://github.com/shaptonstahl/
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21  g1 < graph.atlas(128)
## Not run: plot(g1)
M1 < as.matrix(get.adjacency(g1))
M1
RelationStrengthSimilarity(xadj=M1, v1=5, v2=6, radius=1)
RelationStrengthSimilarity(xadj=M1, v1=5, v2=6, radius=2)
RelationStrengthSimilarity(xadj=M1, v1=5, v2=6, radius=3)
RelationStrengthSimilarity(xadj=M1, v1=5, v2=6, radius=4)
RelationStrengthSimilarity(xadj=M1, radius=2)
TestUndirectedNetwork < function(n) {
M < matrix(runif(n*n), nrow=n)
M < (M + t(M)) / 2
diag(M) < 0
return(M)
}
M2 < TestUndirectedNetwork(75)
system.time(RelationStrengthSimilarity(xadj=M2, directed=FALSE, method="BetterR")) # all R
system.time(RelationStrengthSimilarity(xadj=M2, directed=FALSE)) # Rcpp

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.
Please suggest features or report bugs with the GitHub issue tracker.
All documentation is copyright its authors; we didn't write any of that.