  Mutualism is a biological interaction mutually beneficial for both species
involved, such as the interaction between plants and their pollinators. Real
mutualistic communities can be understood as weighted bipartite networks and
they present a nested structure and truncated power law degree and strength
distributions. We present a novel link aggregation model that works on a
strength-preferential attachment rule based on the Individual Neutrality
hypothesis. The model generates mutualistic networks with emergent nestedness
and truncated distributions. We provide some analytical results and compare the
simulated and empirical network topology. Upon further improving the shape of
the distributions, we have also studied the role of forbidden interactions on
the model and found that the inclusion of forbidden links does not prevent for
the appearance of super-generalist species. A Python script with the model
algorithms is available.
