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
adj_mat=rbind(c(0,1,2,0,0,0,0,0,0), #A's neighbors
                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
                )
rownames(adj_mat)=c("A","B","C","D","E","F","G","H","I")
colnames(adj_mat)=c("A","B","C","D","E","F","G","H","I")
G=vector(mode="list", length=ncol(adj_mat))
names(G)=colnames(adj_mat)
S=names(G)[seq_len(3)]
ranks=multiNode.getNodeRanks(S, G, p1=0.9, thresholdDiff=0.01, adj_mat)

[Package CTD version 1.0.0 Index]