A likelihood approach to analysis of network data
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A likelihood approach to analysis of network data. / Wiuf, Carsten; Brameier, Markus; Hagberg, Oskar; Stumpf, Michael P.H.
I: Proceedings of the National Academy of Sciences of the United States of America, Bind 103, Nr. 20, 16.05.2006, s. 7566-7570.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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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