graph.diffuseP1 {CTD} R Documentation

## Diffuse Probability P1 from a starting node

### Description

Recursively diffuse probability from a starting node based on the connectivity of the network, representing the likelihood that a variable is most influenced by a perturbation in the starting node.

### Usage

graph.diffuseP1(p1,sn,G,vNodes,thresholdDiff,adj_mat,verbose=FALSE,
out_dir="",r_level=1,coords=NULL)


### Arguments

 p1 - The probability being dispersed from the starting node, sn, which is preferentially distributed between network nodes by the probability diffusion algorithm based solely on network connectivity. sn - "Start node", or the node most recently visited by the network walker, from which p1 gets dispersed. G - A list of probabilities, with names of the list being the node names in the network. vNodes - "Visited nodes", or the history of previous draws in the node ranking sequence. thresholdDiff - When the probability diffusion algorithm exchanges this amount (thresholdDiff) 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. verbose - If debugging or tracking a diffusion event, verbose=TRUE will activate print statements. Default is FALSE. out_dir - If specified, a image sequence will generate in the output directory specified. r_level - "Recursion level", or the current depth in the call stack caused by a recursive algorithm. Only relevant if out_dir is specified. coords - The x and y coordinates for each node in the network, to remain static between images. Only relevant if out_dir is specified.

### Value

G - A list of returned probabilities after the diffusion of probability has truncated, with names of the list being the node names in the network.

### 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
)