# Measuring the nestedness of a network

The goal of this use case is to (i) measure the nestedness of a bipartite network and (ii) evaluate whether it differs from the random expectation. We will use the `ollerton`

data, which are reasonably small, and the `η`

measure of nestedness (note that `nodf`

is also available).

using EcologicalNetwork # Get the data in an object N = ollerton(); # We will create a function to return the nestedness of the entire # network instead of an array of nestedness values nest = (x) -> η(x)[1] # We will now generate a series of random networks preserving the degree # distribution S = nullmodel(null2(N)); # There is a function to apply a test rapidly to randomized networks. In this # situation we are interested in testing the fact that the network is more # nested than expected by chance. results = test_network_property(N, nest, S, test=:greater); # We can print the results println( "The original network has a nestedness of ", round(nest(N), 3), ",\n", "which is greater than expected by chance (p ~ ", round(results.pval, 4), ") -- ", results.n, " random networks." )

The original network has a nestedness of 0.641, which is greater than expected by chance (p ~ 0.0) -- 301 random networks.

In this simple example, we used `nullmodel`

to generate random realizations of a network, and `test_network_property`

to evaluate whether the observed nestedness was observed by chance. As it stands, all randomized networks had *lower* values, and so the *p*-value is (essentially) null. In short, this network is significantly more nested than expected by chance knowing its degree distribution.