True scale-free networks hidden by finite size effects.
degree distribution
finite size scaling
network form
power laws
statistical physics
Journal
Proceedings of the National Academy of Sciences of the United States of America
ISSN: 1091-6490
Titre abrégé: Proc Natl Acad Sci U S A
Pays: United States
ID NLM: 7505876
Informations de publication
Date de publication:
12 01 2021
12 01 2021
Historique:
entrez:
31
12
2020
pubmed:
1
1
2021
medline:
1
1
2021
Statut:
ppublish
Résumé
We analyze about 200 naturally occurring networks with distinct dynamical origins to formally test whether the commonly assumed hypothesis of an underlying scale-free structure is generally viable. This has recently been questioned on the basis of statistical testing of the validity of power law distributions of network degrees. Specifically, we analyze by finite size scaling analysis the datasets of real networks to check whether the purported departures from power law behavior are due to the finiteness of sample size. We find that a large number of the networks follows a finite size scaling hypothesis without any self-tuning. This is the case of biological protein interaction networks, technological computer and hyperlink networks, and informational networks in general. Marked deviations appear in other cases, especially involving infrastructure and transportation but also in social networks. We conclude that underlying scale invariance properties of many naturally occurring networks are extant features often clouded by finite size effects due to the nature of the sample data.
Identifiants
pubmed: 33380456
pii: 2013825118
doi: 10.1073/pnas.2013825118
pmc: PMC7812829
pii:
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
Copyright © 2021 the Author(s). Published by PNAS.
Déclaration de conflit d'intérêts
The authors declare no competing interest.
Références
Phys Rev E Stat Phys Plasmas Fluids Relat Interdiscip Topics. 1996 Sep;54(3):2291-2297
pubmed: 9965333
Phys Rev E Stat Nonlin Soft Matter Phys. 2006 Jan;73(1 Pt 2):016102
pubmed: 16486211
Nature. 2012 Sep 27;489(7417):537-40
pubmed: 22972194
Cell. 2005 Sep 23;122(6):957-68
pubmed: 16169070
Nature. 2005 Jan 27;433(7024):392-5
pubmed: 15674285
Science. 1999 Apr 2;284(5411):87-9
pubmed: 10102823
Phys Rev Lett. 1987 Jul 27;59(4):381-384
pubmed: 10035754
PLoS One. 2016 Jan 22;11(1):e0147073
pubmed: 26800025
Phys Rev Lett. 2011 Dec 2;107(23):238701
pubmed: 22182132
Phys Rev Lett. 2005 Apr 29;94(16):168101
pubmed: 15904266
Phys Rev Lett. 2000 Nov 20;85(21):4633-6
pubmed: 11082614
Proc Natl Acad Sci U S A. 2005 Mar 22;102(12):4221-4
pubmed: 15767579
PLoS One. 2014 Jan 29;9(1):e85777
pubmed: 24489671
Proc Natl Acad Sci U S A. 2004 Mar 16;101(11):3747-52
pubmed: 15007165
Science. 2012 Feb 10;335(6069):665-6
pubmed: 22323807
Proc Natl Acad Sci U S A. 2002 Oct 1;99(20):12583-8
pubmed: 12239345
PLoS One. 2011;6(7):e20648
pubmed: 21857891
Phys Rev Lett. 2019 Apr 26;122(16):168301
pubmed: 31075025
Science. 2004 Mar 5;303(5663):1538-42
pubmed: 15001784
Phys Rev Lett. 2002 Dec 16;89(25):258702
pubmed: 12484927
Science. 1999 Oct 15;286(5439):509-12
pubmed: 10521342
Proc Natl Acad Sci U S A. 2020 Jun 30;117(26):14812-14818
pubmed: 32541015
Phys Rev Lett. 2001 Jun 11;86(24):5632-5
pubmed: 11415319
PLoS One. 2016 Sep 01;11(9):e0161586
pubmed: 27584596
Nat Commun. 2019 Mar 4;10(1):1017
pubmed: 30833554