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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.

SUBMITTER: Wiuf C

PROVIDER: S-EPMC1472487 | biostudies-literature | 2006 May

REPOSITORIES: biostudies-literature

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A likelihood approach to analysis of network data.

Wiuf Carsten C Brameier Markus M Hagberg Oskar O Stumpf Michael P H MP

Proceedings of the National Academy of Sciences of the United States of America 20060508 20

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 develo ...[more]

PMID: 16682633

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