Literature DB >> 25871069

Universality in the spectral and eigenfunction properties of random networks.

J A Méndez-Bermúdez1, A Alcazar-López1, A J Martínez-Mendoza2, Francisco A Rodrigues3, Thomas K Dm Peron4.   

Abstract

By the use of extensive numerical simulations, we show that the nearest-neighbor energy-level spacing distribution P(s) and the entropic eigenfunction localization length of the adjacency matrices of Erdős-Rényi (ER) fully random networks are universal for fixed average degree ξ≡αN (α and N being the average network connectivity and the network size, respectively). We also demonstrate that the Brody distribution characterizes well P(s) in the transition from α=0, when the vertices in the network are isolated, to α=1, when the network is fully connected. Moreover, we explore the validity of our findings when relaxing the randomness of our network model and show that, in contrast to standard ER networks, ER networks with diagonal disorder also show universality. Finally, we also discuss the spectral and eigenfunction properties of small-world networks.

Entities:  

Year:  2015        PMID: 25871069     DOI: 10.1103/PhysRevE.91.032122

Source DB:  PubMed          Journal:  Phys Rev E Stat Nonlin Soft Matter Phys        ISSN: 1539-3755


  4 in total

1.  Super-Resolution Community Detection for Layer-Aggregated Multilayer Networks.

Authors:  Dane Taylor; Rajmonda S Caceres; Peter J Mucha
Journal:  Phys Rev X       Date:  2017-09-26       Impact factor: 15.762

2.  Structural and spectral properties of generative models for synthetic multilayer air transportation networks.

Authors:  Marzena Fügenschuh; Ralucca Gera; José Antonio Méndez-Bermúdez; Andrea Tagarelli
Journal:  PLoS One       Date:  2021-10-21       Impact factor: 3.240

3.  Information Entropy of Tight-Binding Random Networks with Losses and Gain: Scaling and Universality.

Authors:  C T Martínez-Martínez; J A Méndez-Bermúdez
Journal:  Entropy (Basel)       Date:  2019-01-18       Impact factor: 2.524

4.  Identifying network structure similarity using spectral graph theory.

Authors:  Ralucca Gera; L Alonso; Brian Crawford; Jeffrey House; J A Mendez-Bermudez; Thomas Knuth; Ryan Miller
Journal:  Appl Netw Sci       Date:  2018-01-31
  4 in total

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