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arXiv 2201.04888math.CO
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随机生成图

Generating graphs randomly

Catherine Greenhill

AI总结:

本文针对从特定图族中均匀或近似均匀随机生成图的需求,重点研究具有给定度序列的简单图,介绍多种采样算法,并强调算法需兼具计算效率与严格分析。

摘要:

图在许多学科中用于建模复杂离散系统中对象之间存在的各种关系。研究人员可能希望将感兴趣的网络与来自某个图族(或系综)的“典型”图进行比较,这些图在某种程度上是相似的。实现这一目标的一种方法是从该图族中抽取若干随机图样本,以收集关于“典型”图的信息。因此,需要能够从给定图族中均匀(或近似均匀)随机生成图的算法。由于可能需要大量样本,算法还应具有计算效率。对此类算法的严格分析通常具有挑战性,涉及组合与概率论证。我们将主要关注具有特定度序列的所有简单图集合,并描述几种从该图族中均匀或几乎均匀采样图的算法。

英文摘要:

Graphs are used in many disciplines to model the relationships that exist between objects in a complex discrete system. Researchers may wish to compare a network of interest to a "typical" graph from a family (or ensemble) of graphs which are similar in some way. One way to do this is to take a sample of several random graphs from the family, to gather information about what is "typical". Hence there is a need for algorithms which can generate graphs uniformly (or approximately uniformly) at random from the given family. Since a large sample may be required, the algorithm should also be computationally efficient. Rigorous analysis of such algorithms is often challenging, involving both combinatorial and probabilistic arguments. We will focus mainly on the set of all simple graphs with a particular degree sequence, and describe several different algorithms for sampling graphs from this family uniformly, or almost uniformly.

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。

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