Subnets of scale-free networks are not scale-free: Sampling properties of networks

Research output: Contribution to journal › Journal article › Research › peer-review

Standard

Subnets of scale-free networks are not scale-free : Sampling properties of networks. / Stumpf, Michael P.H.; Wiuf, Carsten; May, Robert M.

In: Proceedings of the National Academy of Sciences of the United States of America, Vol. 102, No. 12, 22.03.2005, p. 4221-4224.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Stumpf, MPH, Wiuf, C & May, RM 2005, 'Subnets of scale-free networks are not scale-free: Sampling properties of networks', Proceedings of the National Academy of Sciences of the United States of America, vol. 102, no. 12, pp. 4221-4224. https://doi.org/10.1073/pnas.0501179102

APA

Stumpf, M. P. H., Wiuf, C., & May, R. M. (2005). Subnets of scale-free networks are not scale-free: Sampling properties of networks. Proceedings of the National Academy of Sciences of the United States of America, 102(12), 4221-4224. https://doi.org/10.1073/pnas.0501179102

Vancouver

Stumpf MPH, Wiuf C, May RM. Subnets of scale-free networks are not scale-free: Sampling properties of networks. Proceedings of the National Academy of Sciences of the United States of America. 2005 Mar 22;102(12):4221-4224. https://doi.org/10.1073/pnas.0501179102

Author

Stumpf, Michael P.H. ; Wiuf, Carsten ; May, Robert M. / Subnets of scale-free networks are not scale-free : Sampling properties of networks. In: Proceedings of the National Academy of Sciences of the United States of America. 2005 ; Vol. 102, No. 12. pp. 4221-4224.

Bibtex

@article{7fe587bddf6f4f58b9d4ef096ee6bf62,
title = "Subnets of scale-free networks are not scale-free: Sampling properties of networks",
abstract = "Most studies of networks have only looked at small subsets of the true network. Here, we discuss the sampling properties of a network's degree distribution under the most parsimonious sampling scheme. Only if the degree distributions of the network and randomly sampled subnets belong to the same family of probability distributions is it possible to extrapolate from subnet data to properties of the global network. We show that this condition is indeed satisfied for some important classes of networks, notably classical random graphs and exponential random graphs. For scale-free degree distributions, however, this is not the case. Thus, inferences about the scale-free nature of a network may have to be treated with some caution. The work presented here has important implications for the analysis of molecular networks as well as for graph theory and the theory of networks in general.",
keywords = "Complex networks, Protein interaction networks, Random graphs, Sampling theory",
author = "Stumpf, {Michael P.H.} and Carsten Wiuf and May, {Robert M.}",
year = "2005",
month = mar,
day = "22",
doi = "10.1073/pnas.0501179102",
language = "English",
volume = "102",
pages = "4221--4224",
journal = "Proceedings of the National Academy of Sciences of the United States of America",
issn = "0027-8424",
publisher = "The National Academy of Sciences of the United States of America",
number = "12",

}

RIS

TY - JOUR

T1 - Subnets of scale-free networks are not scale-free

T2 - Sampling properties of networks

AU - Stumpf, Michael P.H.

AU - Wiuf, Carsten

AU - May, Robert M.

PY - 2005/3/22

Y1 - 2005/3/22

N2 - Most studies of networks have only looked at small subsets of the true network. Here, we discuss the sampling properties of a network's degree distribution under the most parsimonious sampling scheme. Only if the degree distributions of the network and randomly sampled subnets belong to the same family of probability distributions is it possible to extrapolate from subnet data to properties of the global network. We show that this condition is indeed satisfied for some important classes of networks, notably classical random graphs and exponential random graphs. For scale-free degree distributions, however, this is not the case. Thus, inferences about the scale-free nature of a network may have to be treated with some caution. The work presented here has important implications for the analysis of molecular networks as well as for graph theory and the theory of networks in general.

AB - Most studies of networks have only looked at small subsets of the true network. Here, we discuss the sampling properties of a network's degree distribution under the most parsimonious sampling scheme. Only if the degree distributions of the network and randomly sampled subnets belong to the same family of probability distributions is it possible to extrapolate from subnet data to properties of the global network. We show that this condition is indeed satisfied for some important classes of networks, notably classical random graphs and exponential random graphs. For scale-free degree distributions, however, this is not the case. Thus, inferences about the scale-free nature of a network may have to be treated with some caution. The work presented here has important implications for the analysis of molecular networks as well as for graph theory and the theory of networks in general.

KW - Complex networks

KW - Protein interaction networks

KW - Random graphs

KW - Sampling theory

UR - http://www.scopus.com/inward/record.url?scp=15444372528&partnerID=8YFLogxK

U2 - 10.1073/pnas.0501179102

DO - 10.1073/pnas.0501179102

M3 - Journal article

AN - SCOPUS:15444372528

VL - 102

SP - 4221

EP - 4224

JO - Proceedings of the National Academy of Sciences of the United States of America

JF - Proceedings of the National Academy of Sciences of the United States of America

SN - 0027-8424

IS - 12

ER -

ID: 203902905