RelationStrengthSimilarity: Calculate the RSS from one node to another.
In dils: Data-Informed Link Strength. Combine multiple-relationship networks into a single weighted network. Impute (fill-in) missing network links.

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/RelationStrengthSimilarity.R

For a single pair of nodes, implement the RSS algorithm of Chen et al. (2012).

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  RelationStrengthSimilarity(xadj, v1, v2, radius = 3,
    directed = TRUE,
    method = c("Rcpp", "BetterR", "NaiveR"))

`xadj`	numeric matrix, then description of `arg1`.
`v1`	numeric Object type, then description of `arg2`.
`v2`	numeric Object type, then description of `arg2`.
`radius`	numeric, length of longest path examined from `v1` to `v2`.
`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", Hung-Hsuan Chen, Liang Gou, Xiaolong (Luke) Zhang, C. Lee Giles. 2012.

https://github.com/shaptonstahl/

ScalablePCA

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