skip to main content
10.1145/3427796.3433934acmotherconferencesArticle/Chapter ViewAbstractPublication PagesicdcnConference Proceedingsconference-collections
invited-talk

Network Decomposition and Distributed Derandomization

Published:05 January 2021Publication History

ABSTRACT

This keynote talk will provide an overview of a recent line of work [Rozhoň and Ghaffari at STOC 2020; Ghaffari, Harris, and Kuhn at FOCS 2018; and Ghaffari, Kuhn, and Maus at STOC 2017], which presented the first efficient deterministic network decomposition algorithm as well as a general derandomization result for distributed graph algorithms. Informally, the derandomization result shows that any (locally-checkable) graph problem that admits an efficient randomized distributed algorithm also admits an efficient deterministic distributed algorithm. These results resolve several central and decades-old open problems in distributed graph algorithms.

Recommendations

Comments

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Sign in
  • Published in

    cover image ACM Other conferences
    ICDCN '21: Proceedings of the 22nd International Conference on Distributed Computing and Networking
    January 2021
    252 pages
    ISBN:9781450389334
    DOI:10.1145/3427796

    Copyright © 2021 Owner/Author

    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    • Published: 5 January 2021

    Check for updates

    Qualifiers

    • invited-talk
    • Research
    • Refereed limited

PDF Format

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

HTML Format

View this article in HTML Format .

View HTML Format