  Ecological networks are often composed of different sub-communities (often
referred to as modules). Identifying such modules has the potential to develop
a better understanding of the assembly of ecological communities and to
investigate functional overlap or specialisation. The most informative form of
networks are quantitative or weighted networks. Here we introduce an algorithm
to identify modules in quantitative bipartite (or two-mode) networks. It is
based on the hierarchical random graphs concept of Clauset et al. (2008 Nature
453: 98-101) and is extended to include quantitative information and adapted to
work with bipartite graphs. We define the algorithm, which we call QuaBiMo,
sketch its performance on simulated data and illustrate its potential
usefulness with a case study.
