Detecting modules in quantitative bipartite networks: the QuaBiMo algorithm

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.

19 pages, 10 figures, 4 tables

References in corpus (5)

• Modularity and community structure in networks
• Hierarchical structure and the prediction of missing links in networks
• Modularity and community detection in bipartite networks
• Limits of modularity maximization in community detection
• Efficient modularity optimization by multistep greedy algorithm and vertex mover refinement

Cited by in corpus (5)

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• Hierarchical clustering of bipartite data sets based on the statistical significance of coincidences