摘要翻译：
本文给出了具有固定度分布和可调谐度-度相关的网络的指数随机图模型。然后，我们研究了泊松度分布相关网络中渗流转变的性质。发现负相关与非相关网络中的渗流跃迁同属非相关网络的普适类，因此负相关与非相关网络中的渗流跃迁无关。正相关证明是相关的。分类网络中的渗流转变表现为有限团簇的平均大小不发散，在非渗流阶段和临界点，最大团簇的密度和团簇尺寸分布的幂律标度。我们的结果表明，在最近报道的生长网络模型中，异常类型的渗流转变可能是从各种程度-程度相关中继承的。
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英文标题：
《Percolation transition in networks with degree-degree correlation》
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作者：
Jae Dong Noh
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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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英文摘要：
  We introduce an exponential random graph model for networks with a fixed degree distribution and with a tunable degree-degree correlation. We then investigate the nature of a percolation transition in the correlated network with the Poisson degree distribution. It is found that negative correlation is irrelevant in that the percolation transition in the disassortative network belongs to the same universality class of the uncorrelated network. Positive correlation turns out to be relevant. The percolation transition in the assortative network is characterized by the non-diverging mean size of finite clusters and power-law scalings of the density of the largest cluster and the cluster size distribution in the non-percolating phase as well as at the critical point. Our results suggest that the unusual type percolation transition in the growing network models reported recently may be inherited from the assortative degree-degree correlation.
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PDF链接：
https://arxiv.org/pdf/705.0087