摘要翻译：
随机网络集成是真实网络的零模型，广泛用于将真实系统与零假设进行比较。本文研究了任意给定的真实网络中具有相同度分布、相同度相关或相同社团结构的网络集成。我们用这些随机网络系综的熵来刻画这些随机网络系综，即系综中网络总数的归一化对数。我们从一个大的实有向和无向网络集合出发，估计随机系综的熵。我们提出了熵作为评价网络中每个结构特征作用的指标，我们观察到固定无标度分布的集成比均匀度分布的集成具有更小的熵，表明无标度网络中的有序性更高。
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英文标题：
《The entropy of randomized network ensembles》
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作者：
Ginestra Bianconi
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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：
  Randomized network ensembles are the null models of real networks and are extensivelly used to compare a real system to a null hypothesis. In this paper we study network ensembles with the same degree distribution, the same degree-correlations or the same community structure of any given real network. We characterize these randomized network ensembles by their entropy, i.e. the normalized logarithm of the total number of networks which are part of these ensembles.   We estimate the entropy of randomized ensembles starting from a large set of real directed and undirected networks. We propose entropy as an indicator to assess the role of each structural feature in a given real network.We observe that the ensembles with fixed scale-free degree distribution have smaller entropy than the ensembles with homogeneous degree distribution indicating a higher level of order in scale-free networks.
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PDF链接：
https://arxiv.org/pdf/708.0153