Approximating Densest Subgraph in Geometric Intersection Graphs

Sariel Har-Peled, Rahul Saladi
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Abstract

$ \newcommand{\cardin}[1]{\left| {#1} \right|}% \newcommand{\Graph}{\Mh{\mathsf{G}}}% \providecommand{\G}{\Graph}% \renewcommand{\G}{\Graph}% \providecommand{\GA}{\Mh{H}}% \renewcommand{\GA}{\Mh{H}}% \newcommand{\VV}{\Mh{\mathsf{V}}}% \newcommand{\VX}[1]{\VV\pth{#1}}% \providecommand{\EE}{\Mh{\mathsf{E}}}% \renewcommand{\EE}{\Mh{\mathsf{E}}}% \newcommand{\Re}{\mathbb{R}} \newcommand{\reals}{\mathbb{R}} \newcommand{\SetX}{\mathsf{X}} \newcommand{\rad}{r} \newcommand{\Mh}[1]{#1} \newcommand{\query}{q} \newcommand{\eps}{\varepsilon} \newcommand{\VorX}[1]{\mathcal{V} \pth{#1}} \newcommand{\Polygon}{\mathsf{P}} \newcommand{\IntRange}[1]{[ #1 ]} \newcommand{\Space}{\overline{\mathsf{m}}} \newcommand{\pth}[2][\!]{#1\left({#2}\right)} \newcommand{\polylog}{\mathrm{polylog}} \newcommand{\N}{\mathbb N} \newcommand{\Z}{\mathbb Z} \newcommand{\pt}{p} \newcommand{\distY}[2]{\left\| {#1} - {#2} \right\|} \newcommand{\ptq}{q} \newcommand{\pts}{s}$ For an undirected graph $\mathsf{G}=(\mathsf{V}, \mathsf{E})$, with $n$ vertices and $m$ edges, the \emph{densest subgraph} problem, is to compute a subset $S \subseteq \mathsf{V}$ which maximizes the ratio $|\mathsf{E}_S| / |S|$, where $\mathsf{E}_S \subseteq \mathsf{E}$ is the set of all edges of $\mathsf{G}$ with endpoints in $S$. The densest subgraph problem is a well studied problem in computer science. Existing exact and approximation algorithms for computing the densest subgraph require $\Omega(m)$ time. We present near-linear time (in $n$) approximation algorithms for the densest subgraph problem on \emph{implicit} geometric intersection graphs, where the vertices are explicitly given but not the edges. As a concrete example, we consider $n$ disks in the plane with arbitrary radii and present two different approximation algorithms.
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几何交点图中最密集子图的逼近
$\newcommand{cardin}[1]{\left| {#1}\right|}%\newcommand{\Graph}{\Mh{\mathsf{G}}}% \providecommand{\G}{\Graph}%\renewcommand{\G}{\Graph}% \providecommand{\GA}{\Mh{H}}%\renewcommand{\GA}{\Mh{H}}%\newcommand{\VV}{\Mh{\mathsf{V}}}%\newcommand{\VX}[1]{\VV\pth{#1}}%\providecommand{\EE}{\Mh{\mathsf{E}}}%\renewcommand{\EE}{\Mh{\mathsf{E}}}% \newcommand{\Re}{\mathbb{R}}\newcommand{\reals}{\mathbb{R}}\newcommand{\SetX}{\mathsf{X}}\newcommand{\rad}{r}\newcommand{\Mh}[1]{#1}\newcommand{query}{q}\newcommand{\eps}\{varepsilon}\newcommand{\VorX}[1]{\mathcal{V}\pth{#1}}\newcommand{\Polygon}{\mathsf{P}}\newcommand{\IntRange}[1]{[ #1 ]}\newcommand{\Space}{\overline{\mathsf{m}}}\newcommand{\pth}[2][\!]{#1\left({#2}\right)}\newcommand{\polylog}{\mathrm{polylog}}\newcommand{N}{mathbb N}\newcommand{Z}\{mathbb Z}\新命令{pt}{p}。\newcommand{\distY}[2]{\left\|{#1}- {#2}\right\|}\newcommand{ptq}{q}\newcommand{pts}{s}$ 对于一个无向图 $\mathsf{G}=(\mathsf{V}, \mathsf{E})$, 有$n$顶点和$m$边,emph{densest subgraph}问题、是计算一个子集 $S\subseteq \mathsf{V}$,它能使比率 $|mathsf{E}_S| / |S|$最大化,其中$mathsf{E}_S \subseteq \mathsf{E}$是$mathsf{G}$中所有端点在$S$的边的集合。最密子图问题是计算机科学中一个研究得很透彻的问题。计算最密集子图的现有精确算法和近似算法都需要 $\Omega(m)$ 时间。我们提出了几何交集图上最密子图问题的近线性时间($n$)近似算法,其中顶点是明确给出的,但边没有给出。作为一个具体例子,我们考虑了平面上具有任意半径的 $n$ 圆盘,并提出了两种不同的近似算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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