摘要翻译：
随机网络是研究复杂网络性质的一种常用的零模型。我们描述了一种高效而精确的生成任意两点相关无向随机网络的算法，该算法在顶点之间没有自边或多边。为了系统地研究两点相关性的影响，我们进一步发展了一种构造联合度分布$P(j，k)$的形式，它允许同时确定任意的度分布$P(k)$和任意的平均近邻函数$\kN(k)$。以无标度网络($P(k)\propto k^{-\gamma}）和经验复杂网络($P(k)$取自网络）为例，证明了该方法的有效性。最后，我们将我们的算法推广到退火网络，使得网络可以用类似平均场的方式表示。
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英文标题：
《Generation of arbitrarily two-point correlated random networks》
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作者：
Sebastian Weber and Markus Porto
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最新提交年份：
2007
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分类信息：

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：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：
  Random networks are intensively used as null models to investigate properties of complex networks. We describe an efficient and accurate algorithm to generate arbitrarily two-point correlated undirected random networks without self- or multiple-edges among vertices. With the goal to systematically investigate the influence of two-point correlations, we furthermore develop a formalism to construct a joint degree distribution $P(j,k)$ which allows to fix an arbitrary degree distribution $P(k)$ and an arbitrary average nearest neighbor function $\knn(k)$ simultaneously. Using the presented algorithm, this formalism is demonstrated with scale-free networks ($P(k) \propto k^{-\gamma}$) and empirical complex networks ($P(k)$ taken from network) as examples. Finally, we generalize our algorithm to annealed networks which allows networks to be represented in a mean-field like manner.
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PDF链接：
https://arxiv.org/pdf/708.4161