Non-local probes do not help with many graph problems

Mika Göös, Juho Hirvonen, Reut Levi, Moti Medina, Jukka Suomela

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Scopus citations

Abstract

This work bridges the gap between distributed and centralised models of computing in the context of sublinear-time graph algorithms. A priori, typical centralised models of computing (e.g., parallel decision trees or centralised local algorithms) seem to be much more powerful than distributed message-passing algorithms: centralised algorithms can directly probe any part of the input, while in distributed algorithms nodes can only communicate with their immediate neighbours. We show that for a large class of graph problems, this extra freedom does not help centralised algorithms at all: efficient stateless deterministic centralised local algorithms can be simulated with efficient distributed message-passing algorithms. In particular, this enables us to transfer existing lower bound results from distributed algorithms to centralised local algorithms.

Original languageEnglish
Title of host publicationDistributed Computing - 30th International Symposium, DISC 2016, Proceedings
EditorsCyril Gavoille, David Ilcinkas
PublisherSpringer Verlag
Pages201-214
Number of pages14
ISBN (Print)9783662534250
DOIs
StatePublished - 1 Jan 2016
Externally publishedYes
Event30th International Symposium on Distributed Computing, DISC 2016 - Paris, France
Duration: 27 Sep 201629 Sep 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9888 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference30th International Symposium on Distributed Computing, DISC 2016
Country/TerritoryFrance
CityParis
Period27/09/1629/09/16

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