Detecting modules in quantitative bipartite networks: the QuaBiMo algorithm

TL;DR

The paper introduces QuaBiMo, an algorithm designed to detect modules in weighted bipartite ecological networks, enhancing understanding of community structure and functional roles.

Contribution

It extends hierarchical random graph methods to include quantitative data and bipartite structures, providing a novel tool for ecological network analysis.

Findings

Algorithm performs well on simulated data

Case study demonstrates practical usefulness

Enhances detection of ecological modules

Abstract

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.