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Please use this identifier to cite or link to this item: http://hdl.handle.net/10481/34877

Title: Inherent directionality explains the lack of feedback loops in empirical networks
Authors: Domínguez-García, Virginia
Pigolotti, Simone
Muñoz Martínez, Miguel Ángel
Issue Date: 2014
Abstract: We explore the hypothesis that the relative abundance of feedback loops in many empirical complex networks is severely reduced owing to the presence of an inherent global directionality. Aimed at quantifying this idea, we propose a simple probabilistic model in which a free parameter γ controls the degree of inherent directionality. Upon strengthening such directionality, the model predicts a drastic reduction in the fraction of loops which are also feedback loops. To test this prediction, we extensively enumerated loops and feedback loops in many empirical biological, ecological and socio-technological directed networks. We show that, in almost all cases, empirical networks have a much smaller fraction of feedback loops than network randomizations. Quite remarkably, this empirical finding is quantitatively reproduced, for all loop lengths, by our model by fitting its only parameter γ. Moreover, the fitted value of γ correlates quite well with another direct measurement of network directionality, performed by means of a novel algorithm. We conclude that the existence of an inherent network directionality provides a parsimonious quantitative explanation for the observed lack of feedback loops in empirical networks.
Publisher: Nature Publishing Group
Keywords: Complexity
Computational science
URI: http://hdl.handle.net/10481/34877
ISSN: 2045-2322
Rights : Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License
Citation: Domínguez-García, V.; Pigolotti, S.; Muñoz Martínez, M.A. Inherent directionality explains the lack of feedback loops in empirical networks. Scientific Reports, 4: 7497 (2014). [http://hdl.handle.net/10481/34877]
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