基于最短路径分解的度量嵌入

Ittai Abraham, Arnold Filtser, Anupam Gupta, Ofer Neiman
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引用次数: 17

摘要

研究了将路径宽度为k的加权图嵌入到p空间中的问题。我们的主要结果是一个0 (kmin{1p,12})失真嵌入。对于p=1,这是对Lee和Sidiropoulos的最佳上界的一个超指数改进。对于任意固定的p >1,我们的畸变界是渐近紧的。我们的结果是通过一种新的嵌入技术获得的,该技术基于通过最短路径对图进行低深度分解。核心的新思想是,给定一个测地线最短路径P,我们可以概率地将所有点嵌入到关于P的二维空间中。对于P >2,我们的嵌入也意味着改善了有界树宽图(O((klogn)1p))的失真。对于渐近较大的p,我们的结果还暗示了在不包含一个次要项的图上改进的畸变。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Metric embedding via shortest path decompositions
We study the problem of embedding weighted graphs of pathwidth k into ℓp spaces. Our main result is an O(kmin{1p,12})-distortion embedding. For p=1, this is a super-exponential improvement over the best previous bound of Lee and Sidiropoulos. Our distortion bound is asymptotically tight for any fixed p >1. Our result is obtained via a novel embedding technique that is based on low depth decompositions of a graph via shortest paths. The core new idea is that given a geodesic shortest path P, we can probabilistically embed all points into 2 dimensions with respect to P. For p>2 our embedding also implies improved distortion on bounded treewidth graphs (O((klogn)1p)). For asymptotically large p, our results also implies improved distortion on graphs excluding a minor.
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