Coding in undirected graphs is either very helpful or not helpful at all

Mark Braverman, Sumegha Garg, Ariel Schvartzman

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

While it is known that using network coding can significantly improve the throughput of directed networks, it is a notorious open problem whether coding yields any advantage over the multicommodity flow (MCF) rate in undirected networks. It was conjectured in [11] that the answer is 'no'. In this paper we show that even a small advantage over MCF can be amplified to yield a near-maximum possible gap. We prove that any undirected network with k source-sink pairs that exhibits a (1 + ϵ) gap between its MCF rate and its network coding rate can be used to construct a family of graphs G0 whose gap is log(|G'|)c for some constant c < 1. The resulting gap is close to the best currently known upper bound, log(|G'|), which follows from the connection between MCF and sparsest cuts. Our construction relies on a gap-Amplifying graph tensor product that, given two graphs G1,G2 with small gaps, creates another graph G with a gap that is equal to the product of the previous two, at the cost of increasing the size of the graph. We iterate this process to obtain a gap of log(|G'|)c from any initial gap.

Original languageEnglish (US)
Title of host publication8th Innovations in Theoretical Computer Science Conference, ITCS 2017
EditorsChristos H. Papadimitriou
PublisherSchloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing
ISBN (Electronic)9783959770293
DOIs
StatePublished - Nov 1 2017
Event8th Innovations in Theoretical Computer Science Conference, ITCS 2017 - Berkeley, United States
Duration: Jan 9 2017Jan 11 2017

Publication series

NameLeibniz International Proceedings in Informatics, LIPIcs
Volume67
ISSN (Print)1868-8969

Other

Other8th Innovations in Theoretical Computer Science Conference, ITCS 2017
CountryUnited States
CityBerkeley
Period1/9/171/11/17

All Science Journal Classification (ASJC) codes

  • Software

Keywords

  • Gap Amplification
  • Multicommodity flows
  • Network coding

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