摘要翻译：
我们引入了一个增长网络的随机模型，其中加入网络的新节点数和连接数都是随机变化的。我们给出了该模型与零程过程之间的精确映射，并利用该映射导出了任意给定演化规则的度分布的解析解。人们还可以利用这种映射来推断给定网络的可能演化规则。我们在酿酒酵母的蛋白质-蛋白质相互作用(PPI)网络中证明了这一点。
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英文标题：
《Analytical results for stochastically growing networks: connection to
  the zero range process》
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作者：
P. K. Mohanty and Sarika Jalan
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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：
  We introduce a stochastic model of growing networks where both, the number of new nodes which joins the network and the number of connections, vary stochastically. We provide an exact mapping between this model and zero range process, and use this mapping to derive an analytical solution of degree distribution for any given evolution rule. One can also use this mapping to infer about a possible evolution rule for a given network. We demonstrate this for protein-protein interaction (PPI) network for Saccharomyces Cerevisiae.
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PDF链接：
https://arxiv.org/pdf/707.1246