graph.netWalkSnapShot {CTD} | R Documentation |
A network walker steps towards the node that inherited the highest probability from the last node that it stepped into.
graph.netWalkSnapShot(adj_mat,G,output_dir,p1,visitedNodes,S, coords,imgNum=1,useLabels=TRUE)
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. |
visitedNodes |
- A character vector of node names, storing the history of previous draws in the node ranking. |
S |
- A character vector of node names in the subset you want the network walker to find. |
coords |
- The x and y coordinates for each node in the network, to remain static between images. |
imgNum |
- The image number for this snapshot. If images are being generated in a sequence, this serves as an iterator for file naming. |
useLabels |
- If TRUE, node names will display next to their respective nodes in the network. If FALSE, node names will not display. |
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# 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") S = c("A", "C") coords = layout.fruchterman.reingold(ig) # Uncomment to run #graph.netWalkSnapShot(adj_mat,G,output_dir=getwd(),p1=1.0, # "A",S,coords,1,TRUE)