M
Marián Boguñá
Researcher at University of Barcelona
Publications - 139
Citations - 13246
Marián Boguñá is an academic researcher from University of Barcelona. The author has contributed to research in topics: Complex network & Degree distribution. The author has an hindex of 48, co-authored 136 publications receiving 11622 citations. Previous affiliations of Marián Boguñá include National Institutes of Health & Petersburg Nuclear Physics Institute.
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Hyperbolic geometry of complex networks
TL;DR: It is shown that targeted transport processes without global topology knowledge are maximally efficient, according to all efficiency measures, in networks with strongest heterogeneity and clustering, and that this efficiency is remarkably robust with respect to even catastrophic disturbances and damages to the network structure.
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Models of social networks based on social distance attachment
TL;DR: Analytical results are derived, showing that the proposed class of models of social network formation reproduces the main statistical characteristics of real social networks: large clustering coefficient, positive degree correlations, and the emergence of a hierarchy of communities.
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Extracting the multiscale backbone of complex weighted networks
TL;DR: A filtering method is defined that offers a practical procedure to extract the relevant connection backbone in complex multiscale networks, preserving the edges that represent statistically significant deviations with respect to a null model for the local assignment of weights to edges.
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Generation of uncorrelated random scale-free networks
TL;DR: This work proposes and analyzes a model capable of generating random uncorrelated scale-free networks with no multiple and self-connections based on the classical configuration model, with an additional restriction on the maximum possible degree of the vertices.
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Topology of the world trade web
Ma Ángeles Serrano,Marián Boguñá +1 more
TL;DR: This network displays the typical properties of complex networks, namely, scale-free degree distribution, the small-world property, a high clustering coefficient, and, in addition, degree-degree correlation between different vertices.