Wiuf Carsten, Brameier Markus, Hagberg Oskar, Stumpf Michael P H
Bioinformatics Research Center, University of Aarhus, Høegh-Guldbergsgade 10, Building 1090, 8000 Aarhus C, Denmark.
Proc Natl Acad Sci U S A. 2006 May 16;103(20):7566-70. doi: 10.1073/pnas.0600061103. Epub 2006 May 8.
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.
生物、社会和技术网络数据通常使用简单的汇总统计量（如观察到的度分布）和非参数自助程序进行分析，以提供用于检验网络假设的适当零分布。在本文中，我们提出了一种全似然方法，该方法使我们能够估计网络增长一般模型的参数，这些模型可以用递归关系来表示。为了处理更大的网络，我们开发了一种重要性抽样方案，该方案使我们能够近似似然性，并对网络及其生成方式进行推断，估计模型中的参数，并对网络数据进行参数自助分析。我们通过估计秀丽隐杆线虫蛋白质相互作用网络的增长参数来说明这种方法的强大之处。
