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).

~~~@example 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." ) ~~~

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.