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摘要翻译：
某些网络的性质是由未显式测量的隐变量决定的。隐变量的给定值的顶点连接到其他顶点的k的条件概率（传播子）决定了所有可测性质。我们研究了隐变量模型，找到了一个平均近似，使我们能够得到传播子的一般解析结果。分析结果表明了该近似的有效性。我们将隐变量模型应用于蛋白质-蛋白质相互作用网络中，隐变量是缔合自由能，它由依赖于生物化学和进化的分布决定。我们计算了不同物种的几个引脚的度分布和聚类系数；与实测数据吻合较好。对于人类相互作用体，两个不同的参数集给出了相同的度分布，但计算出的聚类系数相差约2倍。这表明度分布不足以决定引脚的性质。
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英文标题：
《Clustering Coefficients of Protein-Protein Interaction Networks》
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作者：
Gerald A. Miller, Yi Y. Shi, Hong Qian, and Karol Bomsztyk
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最新提交年份：
2007
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分类信息：
一级分类：Quantitative Biology 数量生物学
二级分类：Quantitative Methods 定量方法
分类描述：All experimental, numerical, statistical and mathematical contributions of value to biology
对生物学价值的所有实验、数值、统计和数学贡献
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一级分类：Physics 物理学
二级分类：Statistical Mechanics 统计力学
分类描述：Phase transitions, thermodynamics, field theory, non-equilibrium phenomena, renormalization group and scaling, integrable models, turbulence
相变，热力学，场论，非平衡现象，重整化群和标度，可积模型，湍流
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一级分类：Physics 物理学
二级分类：Biological Physics 生物物理学
分类描述：Molecular biophysics, cellular biophysics, neurological biophysics, membrane biophysics, single-molecule biophysics, ecological biophysics, quantum phenomena in biological systems (quantum biophysics), theoretical biophysics, molecular dynamics/modeling and simulation, game theory, biomechanics, bioinformatics, microorganisms, virology, evolution, biophysical methods.
分子生物物理、细胞生物物理、神经生物物理、膜生物物理、单分子生物物理、生态生物物理、生物系统中的量子现象（量子生物物理）、理论生物物理、分子动力学/建模与模拟、博弈论、生物力学、生物信息学、微生物、病毒学、进化论、生物物理方法。
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一级分类：Quantitative Biology 数量生物学
二级分类：Molecular Networks 分子网络
分类描述：Gene regulation, signal transduction, proteomics, metabolomics, gene and enzymatic networks
基因调控、信号转导、蛋白质组学、代谢组学、基因和酶网络
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英文摘要：
The properties of certain networks are determined by hidden variables that are not explicitly measured. The conditional probability (propagator) that a vertex with a given value of the hidden variable is connected to k of other vertices determines all measurable properties. We study hidden variable models and find an averaging approximation that enables us to obtain a general analytical result for the propagator. Analytic results showing the validity of the approximation are obtained. We apply hidden variable models to protein-protein interaction networks (PINs) in which the hidden variable is the association free-energy, determined by distributions that depend on biochemistry and evolution. We compute degree distributions as well as clustering coefficients of several PINs of different species; good agreement with measured data is obtained. For the human interactome two different parameter sets give the same degree distributions, but the computed clustering coefficients differ by a factor of about two. This shows that degree distributions are not sufficient to determine the properties of PINs.
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PDF链接：
https://arxiv.org/pdf/704.3748
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