In this example, we will show how the modular structure of an ecological network can be optimized. Finding the optimal modular structure can be a time-consuming process, as it relies on heuristic which are not guaranteed to converge to the global maximum. There is no elegant alternative to trying multiple approaches, repeating the process multiple times, and having some luck.

using EcologicalNetworks
using EcologicalNetworksPlots
using Plots
using Plots.PlotMeasures

Generating modular partitions

For the first approach, we will generate random partitions of the species across 3 to 12 modules, and evaluate 20 replicate attempts for each of these combinations. The output we are interested in is the number of modules, and the overall modularity.

N = convert(BipartiteNetwork, web_of_life("M_PA_003"))

n = repeat(3:12, outer=20)
m = Array{Dict}(undef, length(n))

for i in eachindex(n)
  # Each run returns the network and its modules
  # We discard the network, and assign the modules to our object
  _, m[i] = n_random_modules(n[i])(N) |> x -> brim(x...)
end
q = map(x -> Q(N,x), m);
c = (m .|> values |> collect) .|> unique .|> length;
200-element Array{Int64,1}:
 3
 4
 5
 6
 6
 7
 6
 8
 7
 8
 ⋮
 3
 5
 5
 7
 6
 7
 6
 8
 7
p1 = scatter(c, q, c=:grey, msw=0.0, leg=false, frame=:origin, grid=false, margin = 10mm)
xaxis!(p1, "Number of modules")
yaxis!(p1, "Modularity", (0, 0.5))

Measuring modularity

Now that we have the modular partition for every attempt, we can count the modules in it, and measure its modularity. Out of all attempts, we want to get the most modular one, i.e. the one with highest modularity. In some simple problems, there may be several partitions with the highest value, so we can either take the first, or one at random:

optimal = rand(findall(q.== maximum(q)));
best_m = m[optimal];
Dict{String,Int64} with 39 entries:
  "Pseudomyrmex concolor"      => 6
  "Cecropia purpurascens"      => 2
  "Hirtella myrmecophila"      => 1
  "Allomerus octoarticulatus " => 1
  "Cordia nodosa"              => 7
  "Azteca isthmica"            => 2
  "Allomerus prancei"          => 1
  "Tococa bullifera"           => 4
  "Azteca schummani"           => 6
  "Pourouma heterophylla"      => 3
  "Crematogaster sp2 M_PA_001" => 7
  "Pseudomyrmex nigrescens"    => 6
  "Azteca sp1 M_PA_001"        => 7
  "Azteca sp4 M_PA_001"        => 4
  "Cecropia distachya"         => 2
  "Tachigali polyphylla"       => 6
  "Maieta guianensis"          => 4
  "Azteca sp5 M_PA_001"        => 6
  "Duroia saccifera"           => 1
  ⋮                            => ⋮

This can be plotted using EcologicalNetworksPlots:

I = initial(RandomInitialLayout, N)
for step in 1:4000
  position!(SpringElectric(1.2; gravity=0.1), I, N)
end
p2 = plot(I, N, aspectratio=1)
scatter!(p2, I, N, bipartite=true, nodefill=best_m, markercolor=:isolum)

Species functional roles

We can finally look at the functional roles of the species:

roles = functional_cartography(N, best_m)
Dict{String,Tuple{Float64,Float64}} with 39 entries:
  "Pseudomyrmex concolor"      => (-0.20702, 0.0)
  "Cecropia purpurascens"      => (1.02565, 0.0)
  "Hirtella myrmecophila"      => (-1.11803, 0.0)
  "Allomerus octoarticulatus " => (1.11803, 0.0)
  "Cordia nodosa"              => (2.26779, 0.244898)
  "Azteca isthmica"            => (1.82337, 0.0)
  "Allomerus prancei"          => (-1.11803, 0.0)
  "Tococa bullifera"           => (1.93139, 0.277778)
  "Azteca schummani"           => (-0.20702, 0.0)
  "Pourouma heterophylla"      => (NaN, 0.0)
  "Crematogaster sp2 M_PA_001" => (-0.377964, 0.5)
  "Pseudomyrmex nigrescens"    => (-0.20702, 0.0)
  "Azteca sp1 M_PA_001"        => (-0.377964, 0.0)
  "Azteca sp4 M_PA_001"        => (-1.50219, 0.0)
  "Cecropia distachya"         => (-0.569803, 0.0)
  "Tachigali polyphylla"       => (0.517549, 0.0)
  "Maieta guianensis"          => (0.214599, 0.0)
  "Azteca sp5 M_PA_001"        => (-0.931589, 0.0)
  "Duroia saccifera"           => (0.0, 0.5)
  ⋮                            => ⋮

This function returns a tuple (an unmodifiable set of values) of coordinates for every species, indicating its within-module contribution, and its participation coefficient. These results can be plotted to separate species in module hubs, network hubs, peripherals, and connectors. Note that in the context of ecological networks, this classification is commonly used. It derives from previous work on metabolic networks, which subdivides the plane in 7 (rather than 4) regions. For the sake of completeness, we have added the 7 regions to the plot as well.

plot(Shape([-2, 2.5, 2.5, -2], [0, 0, 0.05, 0.05]), lab="", frame=:box, lc=:grey, opacity=0.3, c=:grey, lw=0.0, grid=false, margin = 10mm) #R1
plot!(Shape([-2, 2.5, 2.5, -2], [0.05, 0.05, 0.62, 0.62]), lab="", c=:transparent) #R2
plot!(Shape([-2, 2.5, 2.5, -2], [0.62, 0.62, 0.80, 0.80]), lab="", lc=:grey, opacity=0.3, c=:grey, lw=0.0) #R3
plot!(Shape([-2, 2.5, 2.5, -2], [0.80, 0.80, 1.0, 1.0]), lab="", c=:transparent) #R4

plot!(Shape([2.5, 3.0, 3.0, 2.5], [0, 0, 0.3, 0.3]), lab="", c=:transparent) #R5
plot!(Shape([2.5, 3.0, 3.0, 2.5], [0.3, 0.3, 0.75, 0.75]), lab="", lc=:grey, opacity=0.3, c=:grey, lw=0.0) #R6
plot!(Shape([2.5, 3.0, 3.0, 2.5], [0.75, 0.75, 1.0, 1.0]), lab="", c=:transparent) #R7

vline!([2.5], c=:black, ls=:dot, lw=2.0)
hline!([0.62], c=:black, ls=:dot, lw=2.0)
collect(values(roles)) |> x -> scatter!(x, leg=false, c=:white)
yaxis!("Among-module connectivity", (0,1))
xaxis!("Within-module degree", (-2, 3))