Sampling properties of random graphs: The degree distribution

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Standard

Sampling properties of random graphs : The degree distribution. / Stumpf, Michael P.H.; Wiuf, Carsten.

I: Physical Review E - Statistical, Nonlinear, and Soft Matter Physics, Bind 72, Nr. 3, 036118, 01.09.2005.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Harvard

Stumpf, MPH & Wiuf, C 2005, 'Sampling properties of random graphs: The degree distribution', Physical Review E - Statistical, Nonlinear, and Soft Matter Physics, bind 72, nr. 3, 036118. https://doi.org/10.1103/PhysRevE.72.036118

APA

Stumpf, M. P. H., & Wiuf, C. (2005). Sampling properties of random graphs: The degree distribution. Physical Review E - Statistical, Nonlinear, and Soft Matter Physics, 72(3), [036118]. https://doi.org/10.1103/PhysRevE.72.036118

Vancouver

Stumpf MPH, Wiuf C. Sampling properties of random graphs: The degree distribution. Physical Review E - Statistical, Nonlinear, and Soft Matter Physics. 2005 sep. 1;72(3). 036118. https://doi.org/10.1103/PhysRevE.72.036118

Author

Stumpf, Michael P.H. ; Wiuf, Carsten. / Sampling properties of random graphs : The degree distribution. I: Physical Review E - Statistical, Nonlinear, and Soft Matter Physics. 2005 ; Bind 72, Nr. 3.

Bibtex

@article{5b1fe5a425e5441c8ef46ba98596f065,
title = "Sampling properties of random graphs: The degree distribution",
abstract = "We discuss two sampling schemes for selecting random subnets from a network, random sampling and connectivity dependent sampling, and investigate how the degree distribution of a node in the network is affected by the two types of sampling. Here we derive a necessary and sufficient condition that guarantees that the degree distributions of the subnet and the true network belong to the same family of probability distributions. For completely random sampling of nodes we find that this condition is satisfied by classical random graphs; for the vast majority of networks this condition will, however, not be met. We furthermore discuss the case where the probability of sampling a node depends on the degree of a node and we find that even classical random graphs are no longer closed under this sampling regime. We conclude by relating the results to real Eschericia coli protein interaction network data.",
author = "Stumpf, {Michael P.H.} and Carsten Wiuf",
year = "2005",
month = sep,
day = "1",
doi = "10.1103/PhysRevE.72.036118",
language = "English",
volume = "72",
journal = "Physical Review E",
issn = "2470-0045",
publisher = "American Physical Society",
number = "3",

}

RIS

TY - JOUR

T1 - Sampling properties of random graphs

T2 - The degree distribution

AU - Stumpf, Michael P.H.

AU - Wiuf, Carsten

PY - 2005/9/1

Y1 - 2005/9/1

N2 - We discuss two sampling schemes for selecting random subnets from a network, random sampling and connectivity dependent sampling, and investigate how the degree distribution of a node in the network is affected by the two types of sampling. Here we derive a necessary and sufficient condition that guarantees that the degree distributions of the subnet and the true network belong to the same family of probability distributions. For completely random sampling of nodes we find that this condition is satisfied by classical random graphs; for the vast majority of networks this condition will, however, not be met. We furthermore discuss the case where the probability of sampling a node depends on the degree of a node and we find that even classical random graphs are no longer closed under this sampling regime. We conclude by relating the results to real Eschericia coli protein interaction network data.

AB - We discuss two sampling schemes for selecting random subnets from a network, random sampling and connectivity dependent sampling, and investigate how the degree distribution of a node in the network is affected by the two types of sampling. Here we derive a necessary and sufficient condition that guarantees that the degree distributions of the subnet and the true network belong to the same family of probability distributions. For completely random sampling of nodes we find that this condition is satisfied by classical random graphs; for the vast majority of networks this condition will, however, not be met. We furthermore discuss the case where the probability of sampling a node depends on the degree of a node and we find that even classical random graphs are no longer closed under this sampling regime. We conclude by relating the results to real Eschericia coli protein interaction network data.

UR - http://www.scopus.com/inward/record.url?scp=28844465436&partnerID=8YFLogxK

U2 - 10.1103/PhysRevE.72.036118

DO - 10.1103/PhysRevE.72.036118

M3 - Journal article

AN - SCOPUS:28844465436

VL - 72

JO - Physical Review E

JF - Physical Review E

SN - 2470-0045

IS - 3

M1 - 036118

ER -

ID: 203903561