2016
DOI: 10.1098/rsos.140536
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Improved community detection in weighted bipartite networks

Abstract: Real-world complex networks are composed of non-random quantitative interactions. Identifying communities of nodes that tend to interact more with each other than the network as a whole is a key research focus across multiple disciplines, yet many community detection algorithms only use information about the presence or absence of interactions between nodes. Weighted modularity is a potential method for evaluating the quality of community partitions in quantitative networks. In this framework, the optimal comm… Show more

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Cited by 353 publications
(286 citation statements)
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“…To explore whether hybrid network structure changed along the agricultural intensification gradient, we calculated species richness, connectance, nestedness and modularity of each of the 16 hybrid networks we created using the observed plant‐pollinator and plant‐herbivore interactions. We used weighted NODF (Almeida‐Neto & Ulrich ) to measure nestedness and Beckett’s weighted modularity, a label propagation algorithm, to measure modularity (Beckett ). We also calculated estimated species richness (Chao) to account for detection bias in species sampling.…”
Section: Methodsmentioning
confidence: 99%
“…To explore whether hybrid network structure changed along the agricultural intensification gradient, we calculated species richness, connectance, nestedness and modularity of each of the 16 hybrid networks we created using the observed plant‐pollinator and plant‐herbivore interactions. We used weighted NODF (Almeida‐Neto & Ulrich ) to measure nestedness and Beckett’s weighted modularity, a label propagation algorithm, to measure modularity (Beckett ). We also calculated estimated species richness (Chao) to account for detection bias in species sampling.…”
Section: Methodsmentioning
confidence: 99%
“…All network metrics and null models, except modularity, robustness and Resilience 75 , were calculated using the R package “bipartite” version 2.06.1 (Dormann, Fründ, Blüthgen, & Gruber, ; R Core Team, ). Modularity was calculated using the LPAwb+ code available on GitHub (https://github.com/sjbeckett/weighted-modularity-LPAwbPLUS; Beckett, ). For each modularity calculation, the LPAwb+ algorithm was run once.…”
Section: Methodsmentioning
confidence: 99%
“…Modularity values are computed by detecting the extent to which the number of interactions between modules is lower than expected based on random interactions. We calculated modularity Q with the algorithm proposed by Beckett () for weighted bipartite networks based on a single model run with 10 7 steps (Schleuning et al, ); five repeated runs yielded identical Q values. Finally, H 2 ′ measures the overall specialization within a network; that is, whether species in a network tend to partition or share their interaction partners (Blüthgen, Menzel, & Blüthgen, ).…”
Section: Methodsmentioning
confidence: 99%