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Marián Boguñá

5 papers hereh-index 3109 citations6 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author2
  • last author3

Across the 5 of 5 papers where every author was matched, so the position is known.

fields
  • physics.soc-ph4
  • cs.LG1
same name
  • Marián Boguñá — 9 papers, h 2
  • Marián Boguñá — 6 papers, h 4
  • Marián Boguñá — 1 paper

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20162024
most citedThe hidden geometry of weighted complex networks

94 citations · 94 across the 5 of their papers we have counts for

collaborators
Showing physics.soc-phShow all

4 papers · 1 filter

physics.soc-ph2024

Renormalization of networks with weak geometric coupling

Jasper van der Kolk, Marián Boguñá, M. Ángeles Serrano

The Renormalization Group is crucial for understanding systems across scales, including complex networks. Renormalizing networks via network geometry, a framework in which their to…

physics.soc-ph2024

Feature-aware ultra-low dimensional reduction of real networks

Robert Jankowski, Pegah Hozhabrierdi, Marián Boguñá +1

In existing models and embedding methods of networked systems, node features describing their qualities are usually overlooked in favor of focusing solely on node connectivity. Thi…

physics.soc-ph2023

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…

physics.soc-ph2016★ 94 cited

The hidden geometry of weighted complex networks

Antoine Allard, M. Ángeles Serrano, Guillermo García-Pérez +1

The topology of many real complex networks has been conjectured to be embedded in hidden metric spaces, where distances between nodes encode their likelihood of being connected. Be…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.