Amb motiu del tancament d'estiu, la validació de documents es reprendrà a partir del 28 d'agost de 2026. Disculpeu les molèsties.
Con motivo del cierre de verano, la validación de documentos se reanudará a partir del 28 de agosto de 2026. Disculpad las molestias
Due to the summer closure, document validation will resume starting August 28, 2026. We apologize for any inconvenience.

Files

701616.pdf (477.86 KB)

Document type

Article

Version

Published version

Publication date

2020-04-14

Publication license

cc-by (c) Boguñá, Marián et al., 2020
Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/179063

Small worlds and clustering in spatial networks

Journal Title

Authors

Director/Tutor

Journal ISSN

Volume Title

Related resource

https://doi.org/10.1103/PhysRevResearch.2.023040

Abstract

Networks with underlying metric spaces attract increasing research attention in network science, statistical physics, applied mathematics, computer science, sociology, and other fields. This attention is further amplified by the current surge of activity in graph embedding. In the vast realm of spatial network models, only a few reproduce even the most basic properties of real-world networks. Here, we focus on three such properties sparsity, small worldness, and clustering and identify the general subclass of spatial homogeneous and heterogeneous network models that are sparse small worlds and that have nonzero clustering in the thermodynamic limit. We rely on the maximum entropy approach in which network links correspond to noninteracting fermions whose energy depends on spatial distances between nodes.

Subject

Física estadística, Sistemes complexos

Subject (English)

Statistical physics, Complex systems

Citation

Collections

Articles publicats en revistes (Física de la Matèria Condensada)

Citation

BOGUÑÁ, Marián, et al. Small worlds and clustering in spatial networks. Physical Review Research. 2020. Vol. 2, num. 2. ISSN 2643-1564. [consulted: 13 of August of 2026]. Available at: https://hdl.handle.net/2445/179063

Export metadata

JSON - METS

Share record