Approximating transitive reductions for directed networks

Piotr Berman, Bhaskar Dasgupta, Marek Karpinski

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

    8 Scopus citations

    Abstract

    We consider minimum equivalent digraph problem, its maximum optimization variant and some non-trivial extensions of these two types of problems motivated by biological and social network applications. We provide -approximation algorithms for all the minimization problems and 2-approximation algorithms for all the maximization problems using appropriate primal-dual polytopes. We also show lower bounds on the integrality gap of the polytope to provide some intuition on the final limit of such approaches. Furthermore, we provide APX-hardness result for all those problems even if the length of all simple cycles is bounded by 5.

    Original languageEnglish (US)
    Title of host publicationAlgorithms and Data Structures - 11th International Symposium, WADS 2009, Proceedings
    Pages74-85
    Number of pages12
    DOIs
    StatePublished - Sep 14 2009
    Event11th International Symposium on Algorithms and Data Structures, WADS 2009 - Banff, AB, Canada
    Duration: Aug 21 2009Aug 23 2009

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume5664 LNCS
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Other

    Other11th International Symposium on Algorithms and Data Structures, WADS 2009
    CountryCanada
    CityBanff, AB
    Period8/21/098/23/09

    All Science Journal Classification (ASJC) codes

    • Theoretical Computer Science
    • Computer Science(all)

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  • Cite this

    Berman, P., Dasgupta, B., & Karpinski, M. (2009). Approximating transitive reductions for directed networks. In Algorithms and Data Structures - 11th International Symposium, WADS 2009, Proceedings (pp. 74-85). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5664 LNCS). https://doi.org/10.1007/978-3-642-03367-4_7