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Geometric renormalization of weighted networks
Muhua Zheng, Guillermo García-Pérez, Marián Boguñá +1
The geometric renormalization technique for complex networks has successfully revealed the multiscale self-similarity of real network topologies and can be applied to generate repl…
Feature-enriched hyperbolic network geometry
Roya Aliakbarisani, M. Ángeles Serrano, Marián Boguñá
Graph-structured data provide a comprehensive description of complex systems, encompassing not only the interactions among nodes but also the intrinsic features that characterize t…
Geometric description of clustering in directed networks
Antoine Allard, M. Ángeles Serrano, Marián Boguñá
First principle network models are crucial to make sense of the intricate topology of real complex networks. While modeling efforts have been quite successful in undirected network…
Detecting the ultra low dimensionality of real networks
Pedro Almagro-Blanco, Marian Boguna, M. Angeles Serrano
Reducing dimension redundancy to find simplifying patterns in high-dimensional datasets and complex networks has become a major endeavor in many scientific fields. However, detecti…
Multiscale Voter Model on Real Networks
Elisenda Ortiz, M. Ángeles Serrano
We introduce the Multiscale Voter Model (MVM) to investigate clan influence at multiple scale -- family, neighborhood, political party... -- in opinion formation on real complex ne…
A geometry-induced topological phase transition in random graphs
Jasper van der Kolk, M. Ángeles Serrano, Marián Boguñá
Clustering $\unicode{x2013}$ the tendency for neighbors of nodes to be connected $\unicode{x2013}$ quantifies the coupling of a complex network to its latent metric space. In rando…