2016
DOI: 10.1098/rspb.2015.2702
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Matching–centrality decomposition and the forecasting of new links in networks

Abstract: Networks play a prominent role in the study of complex systems of interacting entities in biology, sociology, and economics. Despite this diversity, we demonstrate here that a statistical model decomposing networks into matching and centrality components provides a comprehensive and unifying quantification of their architecture. The matching term quantifies the assortative structure in which node makes links with which other node, whereas the centrality term quantifies the number of links that nodes make. We s… Show more

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Cited by 37 publications
(66 citation statements)
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“…Interactions were analysed following the matching‐centrality formalism (Rohr et al., ). Each species was represented by a “matching” and a “centrality” component.…”
Section: Methodsmentioning
confidence: 99%
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“…Interactions were analysed following the matching‐centrality formalism (Rohr et al., ). Each species was represented by a “matching” and a “centrality” component.…”
Section: Methodsmentioning
confidence: 99%
“…accounts for their respective specificity/generality relative to the number of associated prey or predators. The probability of interaction P ( L ij = 1) based on a general linear model (GLM) follows a log‐linear model of the form (Rohr et al., ):logitfalse(P(Lij=1)false)=λ(vifj)2+normalδivi+normalδ2fjwhere λ, δ 1 and δ 2 are parameters describing the importance of the “matching” ( v i − f j ) 2 and of the “centrality” of prey (vi) and predators (fi). Ecologically, the v term represents the vulnerability of the prey, while the f term represents the foraging ability of the predator (Rohr & Bascompte, ).…”
Section: Methodsmentioning
confidence: 99%
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“…; Rohr et al . ). First, field observations are used to establish which species interact, usually based on direct observation of interactions or proxies of species visitation.…”
Section: Introductionmentioning
confidence: 97%
“…We use a resampling procedure that is able to generate a large gradient of inter-agent resource-competition networks while preserving the total number of interactions in the network [29].…”
Section: Appendix B Alternative Inter-agent Resourcecompetition Netwmentioning
confidence: 99%