作为真实网络的代理或空模型的定制图系综I：量化结构的工具
Tailored graph ensembles as proxies or null models for real networks I: tools for quantifying structure
摘要：
我们研究了结构化随机图集合对真实网络的剪裁，目的是生成精确而实用的数学工具，用于宏观地量化和比较网络拓扑，超越度统计的水平。我们的系综族可以产生具有任何规定的度分布和任何度-度关联函数的图，其控制参数可以完全解析地计算，因此我们可以计算（渐近）熵和复杂性的公式，以及网络之间的信息论距离，直接和明确地表示在测量的度分布和度相关性方面。
英文摘要：
We study the tailoring of structured random graph ensembles to real networks, with the objective of generating precise and practical mathematical tools for quantifying and comparing network topologies macroscopically, beyond the level of degree statistics. Our family of ensembles can produce graphs with any prescribed degree distribution and any degree-degree correlation function, its control parameters can be calculated fully analytically, and as a result we can calculate (asymptotically) formulae for entropies and complexities, and for information-theoretic distances between networks, expressed directly and explicitly in terms of their measured degree distribution and degree correlations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
