# multiNode.getNodeRanks: Generate the "adaptive walk" node rankings, starting from a... In BRL-BCM/CTD: CTD method for “connecting the dots” in weighted graphs

## Description

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

## Usage

 `1` ```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

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15``` ```# 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.