On Sparsest Cut and Conductance in Directed Polymatroidal Networks

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Autori principali: Chekuri, Chandra, Louis, Anand
Natura: Preprint
Pubblicazione: 2024
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author Chekuri, Chandra
Louis, Anand
author_facet Chekuri, Chandra
Louis, Anand
contents We consider algorithms and spectral bounds for sparsest cut and conductance in directed polymatrodal networks. This is motivated by recent work on submodular hypergraphs \cite{Yoshida19,LiM18,ChenOT23,Veldt23} and previous work on multicommodity flows and cuts in polymatrodial networks \cite{ChekuriKRV15}. We obtain three results. First, we obtain an $O(\sqrt{\log n})$-approximation for sparsest cut and point out how this generalizes the result in \cite{ChenOT23}. Second, we consider the symmetric version of conductance and obtain an $O(\sqrt{OPT \log r})$ approximation where $r$ is the maximum degree and we point out how this generalizes previous work on vertex expansion in graphs. Third, we prove a non-constructive Cheeger like inequality that generalizes previous work on hypergraphs. We provide a unified treatment via line-embeddings which were shown to be effective for submodular cuts in \cite{ChekuriKRV15}.
format Preprint
id arxiv_https___arxiv_org_abs_2410_20525
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle On Sparsest Cut and Conductance in Directed Polymatroidal Networks
Chekuri, Chandra
Louis, Anand
Data Structures and Algorithms
We consider algorithms and spectral bounds for sparsest cut and conductance in directed polymatrodal networks. This is motivated by recent work on submodular hypergraphs \cite{Yoshida19,LiM18,ChenOT23,Veldt23} and previous work on multicommodity flows and cuts in polymatrodial networks \cite{ChekuriKRV15}. We obtain three results. First, we obtain an $O(\sqrt{\log n})$-approximation for sparsest cut and point out how this generalizes the result in \cite{ChenOT23}. Second, we consider the symmetric version of conductance and obtain an $O(\sqrt{OPT \log r})$ approximation where $r$ is the maximum degree and we point out how this generalizes previous work on vertex expansion in graphs. Third, we prove a non-constructive Cheeger like inequality that generalizes previous work on hypergraphs. We provide a unified treatment via line-embeddings which were shown to be effective for submodular cuts in \cite{ChekuriKRV15}.
title On Sparsest Cut and Conductance in Directed Polymatroidal Networks
topic Data Structures and Algorithms
url https://arxiv.org/abs/2410.20525