Please use this identifier to cite or link to this item:
http://hdl.handle.net/2445/179063
Title: | Small worlds and clustering in spatial networks |
Author: | Boguñá, Marián Krioukov, Dmitri Almagro, Pedro Serrano Moral, Ma. Ángeles (María Ángeles) |
Keywords: | Física estadística Sistemes complexos Statistical physics Complex systems |
Issue Date: | 14-Apr-2020 |
Publisher: | American Physical Society |
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. |
Note: | Reproducció del document publicat a: https://doi.org/10.1103/PhysRevResearch.2.023040 |
It is part of: | Physical Review Research, 2020, vol. 2, num. 2 |
URI: | http://hdl.handle.net/2445/179063 |
Related resource: | https://doi.org/10.1103/PhysRevResearch.2.023040 |
ISSN: | 2643-1564 |
Appears in Collections: | Articles publicats en revistes (Física de la Matèria Condensada) |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
701616.pdf | 477.86 kB | Adobe PDF | View/Open |
This item is licensed under a Creative Commons License