arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2007.14508math.PRmath.CO
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块模型的大偏差原理

A large deviation principle for block models

Christian Borgs, Jennifer Chayes, Julia Gaudio, Samantha Petti, Subhabrata Sen

AI总结:

本文研究了稠密块模型随机图的大偏差,将其视为随机图子建立了LDP,并以此分析了正则图同态密度的上尾大偏差,确认了对称与对称破缺相及重入相变现象,最终精确定位了Erdos-Renyi二分图上对称与对称破缺区域的边界。

摘要:

我们开创性地研究了稠密状态下块模型随机图的大偏差。遵循Chatterjee-Varadhan(2011)的方法,我们为稠密块模型建立了一个大偏差原理(LDP),将其视为随机图子。作为我们结果的应用,我们研究了正则图同态密度的上尾大偏差。我们确认了“对称”相的存在,在此相中,以该稀有事件为条件的图,看起来像是一个与生成图子具有相同块大小的块模型。在具体例子中,我们还确认了“对称破缺”区域的存在,在此区域中,条件结构不是具有兼容维度的块模型。这为该问题确认了一种“重入相变”现象——类似于为Erdos-Renyi随机图所建立的现象(Chatterjee-Dey(2010),Chatterjee-Varadhan(2011))。最后,扩展Lubetzky-Zhao(2015)的分析,我们确认了正则图同态密度与Erdos-Renyi二分图上算子范数在对称与对称破缺区域之间的精确边界。

英文摘要:

We initiate a study of large deviations for block model random graphs in the dense regime. Following Chatterjee-Varadhan(2011), we establish an LDP for dense block models, viewed as random graphons. As an application of our result, we study upper tail large deviations for homomorphism densities of regular graphs. We identify the existence of a "symmetric" phase, where the graph, conditioned on the rare event, looks like a block model with the same block sizes as the generating graphon. In specific examples, we also identify the existence of a "symmetry breaking" regime, where the conditional structure is not a block model with compatible dimensions. This identifies a "reentrant phase transition" phenomenon for this problem -- analogous to one established for Erdos-Renyi random graphs (Chatterjee-Dey(2010), Chatterjee-Varadhan(2011)). Finally, extending the analysis of Lubetzky-Zhao(2015), we identify the precise boundary between the symmetry and symmetry breaking regime for homomorphism densities of regular graphs and the operator norm on Erdos-Renyi bipartite graphs.

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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