arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 0909.2948math.COmath.PR
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有向随机几何网络中子图的大数定律

Laws of Large Numbers of Subgraphs in Directed Random Geometric Networks

Yilun Shang

摘要:

给定$\mathbb{R}^2$中的独立随机点$\mathcal{X}_n=\{X_1,...,X_n\}$,根据$\mathbb{R}^2$上的某个概率密度函数$f$画出$\mathcal{X}_n=\{X_1,...,X_n\}$,以及一个截断点$r_n>0$,我们构造了一个顶点集为$\mathcal{X}_n$的随机几何有向图$G(\mathcal{X}_n,\mathcal{Y}_n,r_n)$.每个顶点$X_i$在半径为$r_n$(以顶点$X_i$作为原点)的圆中被随机均匀地分配一个具有中心角$α$和倾角$Y_i$的扇区$S_i$。如果$X_j$落在$S_i$中,则存在从$X_i$到$X_j$的电弧。我们还引入了另一个随机几何有向图$G(\mathcal{X}_n,\mathcal{R}_n)$,它的顶点集为$\mathbb{R}^d$中的$\mathcal{X}_n=\{X_1,...,X_n\}$,$d\ge1$,如果$||X_i-X_j||<R_{n,i}$,则从$X_i$到$X_j$存在一条弧.此处$\{R_{n,i}\}_{i\ge1}$为i.i.d.。随机变量,我们可以取任意范数$||\cdot||$。本文研究了上述两种有向网络中的两类小子图--诱导图和孤立图,这有助于理解许多空间网络(如无线通信网络)的局部拓扑结构。本文给出了子图数的强大数定律,推广了Penrose [Random Geometric Graphs,Oxford University Press,2003]的结果.

英文摘要:

Given independent random points $\mathcal{X}_n=\{X_1,...,X_n\}$ in $\mathbb{R}^2$, drawn according to some probability density function $f$ on $\mathbb{R}^2$, and a cutoff $r_n>0$ we construct a random geometric digraph $G(\mathcal{X}_n,\mathcal{Y}_n,r_n)$ with vertex set $\mathcal{X}_n$. Each vertex $X_i$ is assigned uniformly at random a sector $S_i$, of central angle $α$ with inclination $Y_i$, in a circle of radius $r_n$ (with vertex $X_i$ as the origin). An arc is present from $X_i$ to $X_j$, if $X_j$ falls in $S_i$. We also introduce another random geometric digraph $G(\mathcal{X}_n,\mathcal{R}_n)$ with vertex set $\mathcal{X}_n=\{X_1,...,X_n\}$ in $\mathbb{R}^d$, $d\ge1$ and an arc present from $X_i$ to $X_j$ if $||X_i-X_j||<R_{n,i}$. Here $\{R_{n,i}\}_{i\ge1}$ are i.i.d. random variables and we may take an arbitrary norm $||\cdot||$. In this paper we investigate two kinds of small subgraphs--induced and isolated--in the above two directed networks, which contribute to understanding the local topology of many spatial networks, such as wireless communication networks. We give some strong laws of large numbers of subgraph counts thus extending those results of Penrose [Random Geometric Graphs, Oxford University Press, 2003].

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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