In the study, the similarity and popularity variables are combined to give rise to the hyperbolic geometry of the model.
In the study, the similarity and popularity variables are combined to give rise to the hyperbolic geometry of the model. Reducing redundant information to find simplifying patterns in data sets and complex networks is a scientific challenge in many knowledge fields. Moreover, detecting the dimensionality of the data is still a hard-to-solve problem. An article published in the journal Nature Communications presents a method to infer the dimensionality of complex networks through the application of hyperbolic geometrics, which capture the complexity of relational structures of the real world in many diverse domains. Among the authors of the study are the researchers M. Ángeles Serrano and Marián Boguñá, from the Faculty of Physics and the Institute of Complex Systems of the UB ( UBICS ), and Pedro Almargo, from the Higher Technical School of Engineering of the University of Sevilla. The research study provides a multidimensional hyperbolic model of complex networks that reproduces its connectivity, with an ultra-low and customizable dimensionality for each specific network. This enables a better characterization of its structure —e.g.
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