igraph-help
[Top][All Lists]
Advanced

[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

[igraph] Randomizing graph while maintaining some properties

 From: Steve Lianoglou Subject: [igraph] Randomizing graph while maintaining some properties Date: Mon, 13 Jul 2009 11:42:17 -0700 (PDT) User-agent: G2/1.0

```Hi all,

I was curious if there was a good way to randomize an input graph
while keeping some things constant (like degree distro on edges, for
one).

I wrote a naive/convoluted method that I'm using to attempt to do this
on undirected graphs (pasted below), but it really fails to do it
properly, though I guess it's ok for my requirements. Is there a
better way to go?

Thanks,
-steve

graph.randomize.edges <- function(graph, attr=NULL, do.test=TRUE) {
# Randomizes the connections in the graph keeping the same degree/
node
#
# NOTE: All graph attributes will be stripped, it's your job to use
#       the *.annotate.* functions to put the ones back that you
want.
warning(paste("This method doesn't keep the exact degree
distributions",
"as the original graph, even though it's trying to!"))
mode <- ifelse(is.directed(graph), 'directed', 'upper')
adj <- get.adjacency(graph, attr=attr)
new.adj <- matrix(0, nrow(adj), ncol(adj), dimnames=dimnames(adj))
degrees <- rowSums(adj)

if (mode == 'directed') {
for (i in 1:nrow(adj)) {
new.adj[i,] <- sample(adj[i,])
}
} else {
weights <- adj[upper.tri(adj, diag=TRUE)]   # Use the same weights
from old
weights <- weights[weights != 0]            # graph on new random
edges
so.far <- 0
idxs <- upper.tri(adj, diag=TRUE)
for (i in 1:nrow(adj)) {
if (so.far >= length(weights)) {
break
}
already.connected <- which(new.adj[,i] != 0)
n.to.put <- degrees[i] - length(already.connected)
# cat("[", i, "]: ", n.to.put, '\n', sep='')
if (n.to.put > 0) {
take.start <- so.far + 1
take.weights <- weights[take.start:(take.start + n.to.put -
1)]
so.far <- so.far + length(take.weights)
n.zeros <- ncol(adj) - (i-1) - length(take.weights)
edge.weights <- sample(c(take.weights, rep(0, n.zeros)))
if (any(is.na(edge.weights))) {
# oversampled the edges, the loop will break in the next
iter
warning("NA/NA/NA")
}
new.adj[i,i:ncol(adj)] <- edge.weights
}
}
new.adj[is.na(new.adj)] <- 0              # correction for
oversampling

d <- diag(new.adj)
new.adj <- new.adj + t(new.adj)
diag(new.adj) <- d
}

g.rand <- graph.adjacency(new.adj, mode=mode, weighted=TRUE)

if (do.test) {
# Checks that graph has same number of edges
n.edges <- sum(degree(graph)) == sum(degree(g.rand))
if (!n.edges) {
warning("Not the same number of edges")
}

eq <- all(degree(graph) == degree(g.rand))
if (!all(eq)) {
warning("Degrees for each node don't match")
}

# Check for broken pieces
if (no.clusters(graph) != no.clusters(g.rand)) {
warning("Number of pieces in graphs don't match!")
}
}

g.rand
}

```

reply via email to

 [Prev in Thread] Current Thread [Next in Thread]