multiNode.getNodeRanks {CTD} R Documentation

Generate multi-node node rankings ("adaptive" walk)

Description

This function calculates the node rankings starting from a given node in a subset of nodes in a given network, G.

Usage

multiNode.getNodeRanks(S,G,p1,thresholdDiff,adj_mat,num.misses=NULL,
verbose=FALSE,out_dir="",useLabels=FALSE,
coords=NULL)


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 network. p1 - The probability that is preferentially distributed between network nodes by the probability diffusion algorithm based solely on network connectivity. The remaining probability, 1-p1, is uniformally distributed between network nodes, regardless of connectivity. thresholdDiff - When the probability diffusion algorithm exchanges this amount or less between nodes, the algorithm returns up the call stack. adj_mat - The adjacency matrix that encodes the edge weights for the network, G. 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. out_dir - If specified, a image sequence will generate in the output directory specified. useLabels - If TRUE, node names will display next to their respective nodes in the network. If FALSE, node names will not display. Only relevant if out_dir is specified. coords - The x and y coordinates for each node in the network, to remain static between images.

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

# Read in any network via its adjacency matrix
c(1,0,3,0,0,0,0,0,0), #B's neighbors
c(2,3,0,0,1,0,0,0,0), #C's neighbors
c(0,0,0,0,0,0,1,1,0), #D's neighbors
c(0,0,1,0,0,1,0,0,0), #E's neighbors
c(0,0,0,0,1,0,0,0,0), #F's neighbors
c(0,0,0,1,0,0,0,1,0), #G's neighbors
c(0,0,0,1,0,0,1,0,0), #H's neighbors
c(0,0,0,0,0,0,0,0,0) #I's neighbors
)
S=names(G)[seq_len(3)]