English

On Sparsest Cut and Conductance in Directed Polymatroidal Networks

Data Structures and Algorithms 2024-10-29 v1

Abstract

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(logn)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(OPTlogr)O(\sqrt{OPT \log r}) approximation where rr 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}.

Keywords

Cite

@article{arxiv.2410.20525,
  title  = {On Sparsest Cut and Conductance in Directed Polymatroidal Networks},
  author = {Chandra Chekuri and Anand Louis},
  journal= {arXiv preprint arXiv:2410.20525},
  year   = {2024}
}