multiNode.getNodeRanks: Generate the "adaptive walk" node rankings, starting from a...

Description Usage Arguments Value Examples

View source: R/multiNode.getNodeRanks.r

Description

This function calculates the node rankings starting from a given perturbed variable in a subset of variables in the background knowledge graph.

Usage

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multiNode.getNodeRanks(S, G, num.misses = NULL, verbose = FALSE)

Arguments

S

- A character vector of the node names for the subset of nodes you want to encode.

G

- A list of probabilities with list names being the node names of the background graph.

num.misses

- The number of "misses" the network walker will tolerate before switching to fixed length codes for remaining nodes to be found.

verbose

- If TRUE, print statements will execute as progress is made. Default is FALSE.

Value

ranks - A list of character vectors of node names in the order they were drawn by the probability diffusion algorithm, from each starting node in S.

Examples

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# Read in any network via its adjacency matrix
tmp = matrix(1, nrow=100, ncol=100)
for (i in 1:100) {
  for (j in 1:100) {
    tmp[i, j] = rnorm(1, mean=0, sd=1)
  }
}
colnames(tmp) = sprintf("Compound%d", 1:100)
ig = graph.adjacency(tmp, mode="undirected", weighted=TRUE, add.colnames="name")
V(ig)$name = tolower(V(ig)$name)
# Get node rankings for graph
G = vector(mode="list", length=length(V(ig)$name))
names(G) = V(ig)$name
S = names(G)[1:3]
ranks = multiNode.getNodeRanks(S, G)

BRL-BCM/CTD documentation built on Feb. 28, 2020, 8:11 a.m.