Fluctuations of subgraph counts in graphon based random graphs

Bhaswar B. Bhattacharya, Anirban Chatterjee, Svante Janson
{"title":"Fluctuations of subgraph counts in graphon based random graphs","authors":"Bhaswar B. Bhattacharya, Anirban Chatterjee, Svante Janson","doi":"10.1017/s0963548322000335","DOIUrl":null,"url":null,"abstract":"<p>Given a graphon <span>\n<span>\n<img data-mimesubtype=\"png\" data-type=\"\" src=\"https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20230406132554462-0824:S0963548322000335:S0963548322000335_inline1.png\"/>\n<span data-mathjax-type=\"texmath\"><span>\n$W$\n</span></span>\n</span>\n</span> and a finite simple graph <span>\n<span>\n<img data-mimesubtype=\"png\" data-type=\"\" src=\"https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20230406132554462-0824:S0963548322000335:S0963548322000335_inline2.png\"/>\n<span data-mathjax-type=\"texmath\"><span>\n$H$\n</span></span>\n</span>\n</span>, with vertex set <span>\n<span>\n<img data-mimesubtype=\"png\" data-type=\"\" src=\"https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20230406132554462-0824:S0963548322000335:S0963548322000335_inline3.png\"/>\n<span data-mathjax-type=\"texmath\"><span>\n$V(H)$\n</span></span>\n</span>\n</span>, denote by <span>\n<span>\n<img data-mimesubtype=\"png\" data-type=\"\" src=\"https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20230406132554462-0824:S0963548322000335:S0963548322000335_inline4.png\"/>\n<span data-mathjax-type=\"texmath\"><span>\n$X_n(H, W)$\n</span></span>\n</span>\n</span> the number of copies of <span>\n<span>\n<img data-mimesubtype=\"png\" data-type=\"\" src=\"https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20230406132554462-0824:S0963548322000335:S0963548322000335_inline5.png\"/>\n<span data-mathjax-type=\"texmath\"><span>\n$H$\n</span></span>\n</span>\n</span> in a <span>\n<span>\n<img data-mimesubtype=\"png\" data-type=\"\" src=\"https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20230406132554462-0824:S0963548322000335:S0963548322000335_inline6.png\"/>\n<span data-mathjax-type=\"texmath\"><span>\n$W$\n</span></span>\n</span>\n</span>-random graph on <span>\n<span>\n<img data-mimesubtype=\"png\" data-type=\"\" src=\"https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20230406132554462-0824:S0963548322000335:S0963548322000335_inline7.png\"/>\n<span data-mathjax-type=\"texmath\"><span>\n$n$\n</span></span>\n</span>\n</span> vertices. The asymptotic distribution of <span>\n<span>\n<img data-mimesubtype=\"png\" data-type=\"\" src=\"https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20230406132554462-0824:S0963548322000335:S0963548322000335_inline8.png\"/>\n<span data-mathjax-type=\"texmath\"><span>\n$X_n(H, W)$\n</span></span>\n</span>\n</span> was recently obtained by Hladký, Pelekis, and Šileikis [17] in the case where <span>\n<span>\n<img data-mimesubtype=\"png\" data-type=\"\" src=\"https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20230406132554462-0824:S0963548322000335:S0963548322000335_inline9.png\"/>\n<span data-mathjax-type=\"texmath\"><span>\n$H$\n</span></span>\n</span>\n</span> is a clique. In this paper, we extend this result to any fixed graph <span>\n<span>\n<img data-mimesubtype=\"png\" data-type=\"\" src=\"https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20230406132554462-0824:S0963548322000335:S0963548322000335_inline10.png\"/>\n<span data-mathjax-type=\"texmath\"><span>\n$H$\n</span></span>\n</span>\n</span>. Towards this we introduce a notion of <span>\n<span>\n<img data-mimesubtype=\"png\" data-type=\"\" src=\"https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20230406132554462-0824:S0963548322000335:S0963548322000335_inline11.png\"/>\n<span data-mathjax-type=\"texmath\"><span>\n$H$\n</span></span>\n</span>\n</span>-regularity of graphons and show that if the graphon <span>\n<span>\n<img data-mimesubtype=\"png\" data-type=\"\" src=\"https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20230406132554462-0824:S0963548322000335:S0963548322000335_inline12.png\"/>\n<span data-mathjax-type=\"texmath\"><span>\n$W$\n</span></span>\n</span>\n</span> is not <span>\n<span>\n<img data-mimesubtype=\"png\" data-type=\"\" src=\"https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20230406132554462-0824:S0963548322000335:S0963548322000335_inline13.png\"/>\n<span data-mathjax-type=\"texmath\"><span>\n$H$\n</span></span>\n</span>\n</span>-regular, then <span>\n<span>\n<img data-mimesubtype=\"png\" data-type=\"\" src=\"https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20230406132554462-0824:S0963548322000335:S0963548322000335_inline14.png\"/>\n<span data-mathjax-type=\"texmath\"><span>\n$X_n(H, W)$\n</span></span>\n</span>\n</span> has Gaussian fluctuations with scaling <span>\n<span>\n<img data-mimesubtype=\"png\" data-type=\"\" src=\"https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20230406132554462-0824:S0963548322000335:S0963548322000335_inline15.png\"/>\n<span data-mathjax-type=\"texmath\"><span>\n$n^{|V(H)|-\\frac{1}{2}}$\n</span></span>\n</span>\n</span>. On the other hand, if <span>\n<span>\n<img data-mimesubtype=\"png\" data-type=\"\" src=\"https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20230406132554462-0824:S0963548322000335:S0963548322000335_inline16.png\"/>\n<span data-mathjax-type=\"texmath\"><span>\n$W$\n</span></span>\n</span>\n</span> is <span>\n<span>\n<img data-mimesubtype=\"png\" data-type=\"\" src=\"https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20230406132554462-0824:S0963548322000335:S0963548322000335_inline17.png\"/>\n<span data-mathjax-type=\"texmath\"><span>\n$H$\n</span></span>\n</span>\n</span>-regular, then the fluctuations are of order <span>\n<span>\n<img data-mimesubtype=\"png\" data-type=\"\" src=\"https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20230406132554462-0824:S0963548322000335:S0963548322000335_inline18.png\"/>\n<span data-mathjax-type=\"texmath\"><span>\n$n^{|V(H)|-1}$\n</span></span>\n</span>\n</span> and the limiting distribution of <span>\n<span>\n<img data-mimesubtype=\"png\" data-type=\"\" src=\"https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20230406132554462-0824:S0963548322000335:S0963548322000335_inline19.png\"/>\n<span data-mathjax-type=\"texmath\"><span>\n$X_n(H, W)$\n</span></span>\n</span>\n</span> can have both Gaussian and non-Gaussian components, where the non-Gaussian component is a (possibly) infinite weighted sum of centred chi-squared random variables with the weights determined by the spectral properties of a graphon derived from <span>\n<span>\n<img data-mimesubtype=\"png\" data-type=\"\" src=\"https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20230406132554462-0824:S0963548322000335:S0963548322000335_inline20.png\"/>\n<span data-mathjax-type=\"texmath\"><span>\n$W$\n</span></span>\n</span>\n</span>. Our proofs use the asymptotic theory of generalised <span>\n<span>\n<img data-mimesubtype=\"png\" data-type=\"\" src=\"https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20230406132554462-0824:S0963548322000335:S0963548322000335_inline21.png\"/>\n<span data-mathjax-type=\"texmath\"><span>\n$U$\n</span></span>\n</span>\n</span>-statistics developed by Janson and Nowicki [22]. We also investigate the structure of <span>\n<span>\n<img data-mimesubtype=\"png\" data-type=\"\" src=\"https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20230406132554462-0824:S0963548322000335:S0963548322000335_inline22.png\"/>\n<span data-mathjax-type=\"texmath\"><span>\n$H$\n</span></span>\n</span>\n</span>-regular graphons for which either the Gaussian or the non-Gaussian component of the limiting distribution (but not both) is degenerate. Interestingly, there are also <span>\n<span>\n<img data-mimesubtype=\"png\" data-type=\"\" src=\"https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20230406132554462-0824:S0963548322000335:S0963548322000335_inline23.png\"/>\n<span data-mathjax-type=\"texmath\"><span>\n$H$\n</span></span>\n</span>\n</span>-regular graphons <span>\n<span>\n<img data-mimesubtype=\"png\" data-type=\"\" src=\"https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20230406132554462-0824:S0963548322000335:S0963548322000335_inline24.png\"/>\n<span data-mathjax-type=\"texmath\"><span>\n$W$\n</span></span>\n</span>\n</span> for which both the Gaussian or the non-Gaussian components are degenerate, that is, <span>\n<span>\n<img data-mimesubtype=\"png\" data-type=\"\" src=\"https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20230406132554462-0824:S0963548322000335:S0963548322000335_inline25.png\"/>\n<span data-mathjax-type=\"texmath\"><span>\n$X_n(H, W)$\n</span></span>\n</span>\n</span> has a degenerate limit even under the scaling <span>\n<span>\n<img data-mimesubtype=\"png\" data-type=\"\" src=\"https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20230406132554462-0824:S0963548322000335:S0963548322000335_inline26.png\"/>\n<span data-mathjax-type=\"texmath\"><span>\n$n^{|V(H)|-1}$\n</span></span>\n</span>\n</span>. We give an example of this degeneracy with <span>\n<span>\n<img data-mimesubtype=\"png\" data-type=\"\" src=\"https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20230406132554462-0824:S0963548322000335:S0963548322000335_inline27.png\"/>\n<span data-mathjax-type=\"texmath\"><span>\n$H=K_{1, 3}$\n</span></span>\n</span>\n</span> (the 3-star) and also establish non-degeneracy in a few examples. This naturally leads to interesting open questions on higher order degeneracies.</p>","PeriodicalId":10503,"journal":{"name":"Combinatorics, Probability and Computing","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Combinatorics, Probability and Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1017/s0963548322000335","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

Given a graphon Abstract Image $W$ and a finite simple graph Abstract Image $H$ , with vertex set Abstract Image $V(H)$ , denote by Abstract Image $X_n(H, W)$ the number of copies of Abstract Image $H$ in a Abstract Image $W$ -random graph on Abstract Image $n$ vertices. The asymptotic distribution of Abstract Image $X_n(H, W)$ was recently obtained by Hladký, Pelekis, and Šileikis [17] in the case where Abstract Image $H$ is a clique. In this paper, we extend this result to any fixed graph Abstract Image $H$ . Towards this we introduce a notion of Abstract Image $H$ -regularity of graphons and show that if the graphon Abstract Image $W$ is not Abstract Image $H$ -regular, then Abstract Image $X_n(H, W)$ has Gaussian fluctuations with scaling Abstract Image $n^{|V(H)|-\frac{1}{2}}$ . On the other hand, if Abstract Image $W$ is Abstract Image $H$ -regular, then the fluctuations are of order Abstract Image $n^{|V(H)|-1}$ and the limiting distribution of Abstract Image $X_n(H, W)$ can have both Gaussian and non-Gaussian components, where the non-Gaussian component is a (possibly) infinite weighted sum of centred chi-squared random variables with the weights determined by the spectral properties of a graphon derived from Abstract Image $W$ . Our proofs use the asymptotic theory of generalised Abstract Image $U$ -statistics developed by Janson and Nowicki [22]. We also investigate the structure of Abstract Image $H$ -regular graphons for which either the Gaussian or the non-Gaussian component of the limiting distribution (but not both) is degenerate. Interestingly, there are also Abstract Image $H$ -regular graphons Abstract Image $W$ for which both the Gaussian or the non-Gaussian components are degenerate, that is, Abstract Image $X_n(H, W)$ has a degenerate limit even under the scaling Abstract Image $n^{|V(H)|-1}$ . We give an example of this degeneracy with Abstract Image $H=K_{1, 3}$ (the 3-star) and also establish non-degeneracy in a few examples. This naturally leads to interesting open questions on higher order degeneracies.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
基于图形的随机图中子图计数的波动
给定一个图$W$和一个具有顶点集$V(H)$的有限简单图$H$,用$X_n(H, W)$表示$W$随机图$H$在$n$顶点上的拷贝数。最近Hladký、Pelekis和Šileikis[17]得到了$X_n(H, W)$在$H$为团的情况下的渐近分布。本文将此结果推广到任意固定图$H$。为此,我们引入了graphon $H$-正则性的概念,并证明了如果graphon $W$不是$H$-正则,则$X_n(H, W)$具有随缩放$n^{|V(H)|-\frac{1}{2}}$的高斯波动。另一方面,如果$W$是$H$-正则,则波动阶为$n^{|V(H)|-1}$,并且$X_n(H, W)$的极限分布可以同时具有高斯和非高斯分量,其中非高斯分量是中心卡方随机变量的一个(可能)无限加权和,其权重由由$W$衍生的石墨的谱性质决定。我们的证明使用了Janson和Nowicki[22]开发的广义$U$-统计的渐近理论。我们还研究了$H$正则图形的结构,其中一个极限分布的高斯或非高斯分量(但不是两者)是简并的。有趣的是,也有$H$-正则图形$W$,其高斯或非高斯分量都是简并的,即$X_n(H, W)$即使在缩放$n^{|V(H)|-1}$下也有简并极限。我们给出了$H=K_{1,3}$(3星)的简并性的一个例子,并在几个例子中建立了非简并性。这自然导致了关于高阶简并的有趣的开放性问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
A new formula for the determinant and bounds on its tensor and Waring ranks On the Ramsey numbers of daisies I On the Ramsey numbers of daisies II List packing number of bounded degree graphs Counting spanning subgraphs in dense hypergraphs
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1