Hi,

I am trying to use the random_rewire function to generate a new network,

using the 'correlated' model. The network is pretty small (208 vertices,

594 edges - no loops or parallel edges). If I just run it with default

parameters, it rejects about 300-400 of the moves and 10000 iterations

takes about 1 minute. To improve the shuffling a bit, I tried to add

persist=True. Now the problem is, after 2 hours, it has not even completed

a single iteration. I am suspecting that it is stuck in an infinite loop

somewhere, but I cannot figure out why. Would there be any special property

of a graph which causes this?

For reference, these are the two commands I have tried executing:

K = 10000

# Finishes in about 1 minutes

for i in range(K):

graph_tool.generation.random_rewire(testg, model='correlated')

# After 2 hours and counting, still has not finished one iteration

for i in range(K):

graph_tool.generation.random_rewire(testg, model='correlated',

persist=True)

print "%i done" % i

Also, the question I am trying to answer is, given the original graph and

properties, what is the size of the largest connected component observed

when the network is rewired at random, preserving node degree.

Thanks in advance,

David

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