graph.diffusionSnapShot {CTD}

Capture the current state of probability diffusion

Description

Recursively diffuse probability from a starting node based on the connectivity in a network, G, where the probability represents the likelihood that a variable will be influenced by a perturbation in the starting node.

Usage

graph.diffusionSnapShot(adj_mat,G,output_dir,p1,startNode,
                                visitedNodes,recursion_level,coords)

Arguments

adj_mat

- The adjacency matrix that encodes the edge weights for the network, G.

G

- A list of probabilities, with names of the list being the node names in the network.

output_dir

- The local directory at which you want still PNG images to be saved.

p1

- The probability being dispersed from the starting node, startNode, which is preferentially distributed between network nodes by the probability diffusion algorithm based solely on network connectivity.

startNode

- The first variable drawn in the node ranking, from which p1 gets dispersed.

visitedNodes

- A character vector of node names, storing the history of previous draws in the node ranking.

recursion_level

- The current depth in the call stack caused by a recursive algorithm.

coords

- The x and y coordinates for each node in the network, to remain static between images.

Value

0

Examples

# 7 node example graph illustrating diffusion of probability based on
# network connectivity.
adj_mat = rbind(c(0,2,1,0,0,0,0), # A
                c(2,0,1,0,0,0,0), # B
                c(1,0,0,1,0,0,0), # C
                c(0,0,1,0,2,0,0), # D
                c(0,0,0,2,0,2,1), # E
                c(0,0,0,1,2,0,1), # F
                c(0,0,0,0,1,1,0)  # G
                )
rownames(adj_mat) = c("A", "B", "C", "D", "E", "F", "G")
colnames(adj_mat) = c("A", "B", "C", "D", "E", "F", "G")
ig = graph.adjacency(as.matrix(adj_mat),mode="undirected",weighted=TRUE)
G=vector(mode="list", length=7)
G[seq_len(length(G))] = 0
names(G) = c("A", "B", "C", "D", "E", "F", "G")
coords = layout.fruchterman.reingold(ig)
V(ig)$x = coords[,1]
V(ig)$y = coords[,2]
# Uncomment to run
#graph.diffusionSnapShot(adj_mat,G,getwd(),1.0,"A","A",1,coords)