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A method for detecting modules in quantitative bipartite networks

A method for detecting modules in quantitative bipartite networks Ecological networks are often composed of different subcommunities (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 specialization. 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 QuanBiMo, sketch its performance on simulated data and illustrate its potential usefulness with a case study. Modules are detected with a higher accuracy in simulated quantitative networks than in their binary counterparts. Even at high levels of noise, QuanBiMo still classifies 70% of links correctly as within‐ or between‐modules. Recursively applying the algorithm results in additional information of within‐module organization of the network. The algorithm introduced here must be seen as a considerable improvement over the current standard of algorithms for binary networks. Due to its higher sensitivity, it is likely to lead to be useful for detecting modules in the typically noisy data of ecological networks. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Methods in Ecology and Evolution Wiley

A method for detecting modules in quantitative bipartite networks

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References (107)

Publisher
Wiley
Copyright
Copyright © 2014 British Ecological Society
ISSN
2041-210X
eISSN
2041-210X
DOI
10.1111/2041-210X.12139
Publisher site
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Abstract

Ecological networks are often composed of different subcommunities (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 specialization. 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 QuanBiMo, sketch its performance on simulated data and illustrate its potential usefulness with a case study. Modules are detected with a higher accuracy in simulated quantitative networks than in their binary counterparts. Even at high levels of noise, QuanBiMo still classifies 70% of links correctly as within‐ or between‐modules. Recursively applying the algorithm results in additional information of within‐module organization of the network. The algorithm introduced here must be seen as a considerable improvement over the current standard of algorithms for binary networks. Due to its higher sensitivity, it is likely to lead to be useful for detecting modules in the typically noisy data of ecological networks.

Journal

Methods in Ecology and Evolution – Wiley

Published: Jan 1, 2014

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