A likelihood approach to analysis of network data

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

Standard

A likelihood approach to analysis of network data. / Wiuf, Carsten; Brameier, Markus; Hagberg, Oskar; Stumpf, Michael P.H.

In: Proceedings of the National Academy of Sciences of the United States of America, Vol. 103, No. 20, 16.05.2006, p. 7566-7570.

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

Harvard

Wiuf, C, Brameier, M, Hagberg, O & Stumpf, MPH 2006, 'A likelihood approach to analysis of network data', Proceedings of the National Academy of Sciences of the United States of America, vol. 103, no. 20, pp. 7566-7570. https://doi.org/10.1073/pnas.0600061103

APA

Wiuf, C., Brameier, M., Hagberg, O., & Stumpf, M. P. H. (2006). A likelihood approach to analysis of network data. Proceedings of the National Academy of Sciences of the United States of America, 103(20), 7566-7570. https://doi.org/10.1073/pnas.0600061103

Vancouver

Wiuf C, Brameier M, Hagberg O, Stumpf MPH. A likelihood approach to analysis of network data. Proceedings of the National Academy of Sciences of the United States of America. 2006 May 16;103(20):7566-7570. https://doi.org/10.1073/pnas.0600061103

Author

Wiuf, Carsten ; Brameier, Markus ; Hagberg, Oskar ; Stumpf, Michael P.H. / A likelihood approach to analysis of network data. In: Proceedings of the National Academy of Sciences of the United States of America. 2006 ; Vol. 103, No. 20. pp. 7566-7570.

Bibtex

@article{fb4e86043a024f1a80e241176ac03ef4,
title = "A likelihood approach to analysis of network data",
abstract = "Biological, sociological, and technological network data are often analyzed by using simple summary statistics, such as the observed degree distribution, and nonparametric bootstrap procedures to provide an adequate null distribution for testing hypotheses about the network. In this article we present a full-likelihood approach that allows us to estimate parameters for general models of network growth that can be expressed in terms of recursion relations. To handle larger networks we have developed an importance sampling scheme that allows us to approximate the likelihood and draw inference about the network and how it has been generated, estimate the parameters in the model, and perform parametric bootstrap analysis of network data. We illustrate the power of this approach by estimating growth parameters for the Caenorhabditis elegans protein interaction network.",
keywords = "Biological network, Importance sampling, Likelihood recursion, Network model, Random graph",
author = "Carsten Wiuf and Markus Brameier and Oskar Hagberg and Stumpf, {Michael P.H.}",
year = "2006",
month = may,
day = "16",
doi = "10.1073/pnas.0600061103",
language = "English",
volume = "103",
pages = "7566--7570",
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 = "20",

}

RIS

TY - JOUR

T1 - A likelihood approach to analysis of network data

AU - Wiuf, Carsten

AU - Brameier, Markus

AU - Hagberg, Oskar

AU - Stumpf, Michael P.H.

PY - 2006/5/16

Y1 - 2006/5/16

N2 - Biological, sociological, and technological network data are often analyzed by using simple summary statistics, such as the observed degree distribution, and nonparametric bootstrap procedures to provide an adequate null distribution for testing hypotheses about the network. In this article we present a full-likelihood approach that allows us to estimate parameters for general models of network growth that can be expressed in terms of recursion relations. To handle larger networks we have developed an importance sampling scheme that allows us to approximate the likelihood and draw inference about the network and how it has been generated, estimate the parameters in the model, and perform parametric bootstrap analysis of network data. We illustrate the power of this approach by estimating growth parameters for the Caenorhabditis elegans protein interaction network.

AB - Biological, sociological, and technological network data are often analyzed by using simple summary statistics, such as the observed degree distribution, and nonparametric bootstrap procedures to provide an adequate null distribution for testing hypotheses about the network. In this article we present a full-likelihood approach that allows us to estimate parameters for general models of network growth that can be expressed in terms of recursion relations. To handle larger networks we have developed an importance sampling scheme that allows us to approximate the likelihood and draw inference about the network and how it has been generated, estimate the parameters in the model, and perform parametric bootstrap analysis of network data. We illustrate the power of this approach by estimating growth parameters for the Caenorhabditis elegans protein interaction network.

KW - Biological network

KW - Importance sampling

KW - Likelihood recursion

KW - Network model

KW - Random graph

UR - http://www.scopus.com/inward/record.url?scp=33646745354&partnerID=8YFLogxK

U2 - 10.1073/pnas.0600061103

DO - 10.1073/pnas.0600061103

M3 - Journal article

C2 - 16682633

AN - SCOPUS:33646745354

VL - 103

SP - 7566

EP - 7570

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 - 20

ER -

ID: 203900448