Graph decomposition lemmas and their role in metric embedding methods

Yair Bartal*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

46 Scopus citations

Abstract

We present basic graph decomposition lemmas and show how they apply as key ingredients in the probabilistic embedding theorem stating that every n point metric space probabilistically embeds in ultrametries with distortion O(log n) and in the proof of a similar bound for the spreading metrics paradigm in undirected graphs. This provides a unified framework for these metric methods which have numerous algorithmic applications.

Original languageAmerican English
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsSusanne Albers, Tomasz Radzik
PublisherSpringer Verlag
Pages89-97
Number of pages9
ISBN (Print)3540230254, 9783540230250
DOIs
StatePublished - 2004

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3221
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

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