摘要翻译：
如今，有许多测量方法被设计来捕捉网络结构的不同方面。为了判断某一网络的结构是否符合要求，需要一个参考模型（空模型）。一种常用的零模型是与原始网络具有相同度集的图的集合。在本文中，我们认为这个集合可能不仅仅是一个空模型--它还携带关于原始网络和影响其演化的因素的信息。通过在某种低层网络结构的空间--在我们的例子中是通过分类性和聚类系数来测量的空间--映射出这个集合，我们可以研究观察到的网络离参数空间的有效区域有多近。这样的分析表明，在网络的演化过程中，哪些数量是主动优化的。我们使用四个非常不同的生物网络来举例说明我们的方法。除此之外，我们发现高聚类可能是蛋白质相互作用网络进化的一种力量。我们还发现这四种网络对随机错误和有针对性的攻击都有明显的鲁棒性。
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英文标题：
《Exploring the assortativity-clustering space of a network's degree
  sequence》
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作者：
Petter Holme, Jing Zhao
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最新提交年份：
2006
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分类信息：

一级分类：Quantitative Biology        数量生物学
二级分类：Other Quantitative Biology        其他定量生物学
分类描述：Work in quantitative biology that does not fit into the other q-bio classifications
不适合其他q-bio分类的定量生物学工作
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一级分类：Physics        物理学
二级分类：Disordered Systems and Neural Networks        无序系统与神经网络
分类描述：Glasses and spin glasses; properties of random, aperiodic and quasiperiodic systems; transport in disordered media; localization; phenomena mediated by defects and disorder; neural networks
眼镜和旋转眼镜；随机、非周期和准周期系统的性质；无序介质中的传输；本地化；由缺陷和无序介导的现象；神经网络
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英文摘要：
  Nowadays there is a multitude of measures designed to capture different aspects of network structure. To be able to say if the structure of certain network is expected or not, one needs a reference model (null model). One frequently used null model is the ensemble of graphs with the same set of degrees as the original network. In this paper we argue that this ensemble can be more than just a null model -- it also carries information about the original network and factors that affect its evolution. By mapping out this ensemble in the space of some low-level network structure -- in our case those measured by the assortativity and clustering coefficients -- one can for example study how close to the valid region of the parameter space the observed networks are. Such analysis suggests which quantities are actively optimized during the evolution of the network. We use four very different biological networks to exemplify our method. Among other things, we find that high clustering might be a force in the evolution of protein interaction networks. We also find that all four networks are conspicuously robust to both random errors and targeted attacks.
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PDF链接：
https://arxiv.org/pdf/q-bio/0611020