# 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) -- 288 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.