Hi all,
thank for the answers to all of my questions previously posted.
I need to compare both binary and weighted network indexes from a real
multiple directed network - with loops - to random ones.
Thus, I was thinking ot generate some thousands of random graphs and then to
look at the distribution of the average indexes' values, keeping constant
the number of vertices and edges (163 and 573, respectively). This should be
enough right now.
While for the binary version of my network I was thinking to use:
randomgraph<-erdos.renyi.game(163, 573, type=c("gnm"), directed=TRUE,
loops=TRUE)
I don't know how do to it with the weighted network, is ot say, how to
randomly distribute weights belonging to a set range (i.e.ranging from 0 to
1 billion).
Also, I was wondering if this function to iterate the random graph
generation and storing the average node degree (ND), is correct or not.
nodeDegree<-array()
for (i in 1:3000){
randomgraph<-erdos.renyi.game(163, 573, type=c("gnm"),
directed=TRUE, loops=TRUE)
nodeDegree[i]<-mean(degree(graph))
}
Thanks a lot,
P.s. I've seen that the on line tutorial is growing, thank you.
Umberto
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