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摘要翻译：
随机网络被广泛应用于复杂网络的建模和性质研究。为了得到各种科学领域中所遇到的复杂网络的良好近似，对随机网络的各种统计性质进行调谐的能力是非常重要的。本文提出了一种能够构造任意程度相关网络的算法，该算法具有可调的程度相关聚类。我们用经验网络作为输入对算法进行了验证，并给出了一种在给定程度-程度相关性的情况下确定依赖于程度的聚类函数的简单方法。
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英文标题：
《Generating random networks with given degree-degree correlations and
degree-dependent clustering》
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作者：
Andreas Pusch, 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：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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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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英文摘要：
Random networks are widely used to model complex networks and research their properties. In order to get a good approximation of complex networks encountered in various disciplines of science, the ability to tune various statistical properties of random networks is very important. In this manuscript we present an algorithm which is able to construct arbitrarily degree-degree correlated networks with adjustable degree-dependent clustering. We verify the algorithm by using empirical networks as input and describe additionally a simple way to fix a degree-dependent clustering function if degree-degree correlations are given.
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PDF链接：
https://arxiv.org/pdf/710.3247
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