A method for detecting modules in quantitative bipartite networks

By Carsten F. Dormann, Rouven Strauss. 2013

529 citations recorded by the source corpus.

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.

Subfields: Ecology, Evolution, Behavior and Systematics, Environmental Engineering, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/a82e49e6d5ee50836e1c8d207c8b489a017d59fe

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In Ecology, Evolution, Behavior and Systematics

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Citations in the corpus

Plant functional traits shape multiple ecosystem services, their trade‐offs and synergies in grasslands (2020, methodology): “A bipartite network is made up of two separate groups, of which linkages between members of the different groups but not within groups are possible (Dormann & Strauss, 2014).”
A comparison of network and clustering methods to detect biogeographical regions (2018, methodology): “The ME and modularity SA (implemented in Quanbimo (Dormann and Strauss 2014)) methods can both incorporate edge weights into the analysis.”
Improved community detection in weighted bipartite networks (2016, methodology): “Normalized modularity The modularity values of QB and QW found above are network-specific—properties such as the size and number of links in a network affect the magnitude of modularity that can be found [1,9,10].”
The macroecology of phylogenetically structured hummingbird–plant networks (2015, methodology): “…complementary specialization and modularity, which differ significantly from random interactions among species (Olesen et al., 2007; Blüthgen, 2010; Dalsgaard et al., 2011, 2013; Schleuning et al., 2012, 2014; Trøjelsgaard & Olesen, 2013; Dormann & Strauss, 2014; Sebastián González et al., 2015).”
Morphological and Spatio‐Temporal Mismatches Shape a Neotropical Savanna Plant‐Hummingbird Network (2014, methodology): “Modularity was calculated with the function com- puteModules, setting the number of Markov Chain Monte Carlo (MCMC) moves to yield no improvement before the algorithm stops to 106 steps, which is the default option adequate for our network size (Dormann & Strauss 2014).”
Defining invasiveness and invasibility in ecological networks (2016, methodology): “Finally, the level of modularity was measured using a new algorithmQuanBimo (Dormann and Straub 2014; adapted from Claused and Newman 2008).”