Before the paper real world networks are rarely scale free, studies used to (still do) check for the slope in degree distribution graphs and if the slope fell between 2 and 3, discern that the network is a real world network. The slope of my networks are not between 2 and 3, and so I decided to conduct a Kolmogorov-Smirnov test (KS test) to check if there is a significant change in the degree distributions of my network and random networks (my network is bipartite and hence I used the sample.bipartite function in R to create the random networks) I created with the same number of nodes and edges.
Eventhough I created a set of 500 random networks, KS test being a two sample test, I could only test the degree distribution of my network against one random network at a time. The problem is that the KS test gives me different answers (significant and not significant) for the same network when I compare it with different random network degree distributions from the 500 that I generated.
Is there a possibility to compare the degree distribution of my real world network with the degree distribution of all 500 random networks rather than get differing answers comparing one at a time? I read that Kruskal- Wallace test might be able to do this but I don't know if that's true.
I use graphpad PRISM for my stats analysis and my R is rusty, so please give me a test I can use on PRISM along with an example if possible. Thank you for your help in advance.
Or am I doing the whole thing wrong and KS test cannot be used to compare the distributions between two networks. Any ideas are gladly welcomed.