Sublinear Time Hypergraph Sparsification via Cut and Edge Sampling Queries

Yu Chen; Sanjeev Khanna; Ansh Nagda

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arXiv · 2106.10386

Sublinear Time Hypergraph Sparsification via Cut and Edge Sampling Queries

Yu Chen·Sanjeev Khanna·Ansh Nagda

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Abstract

The problem of sparsifying a graph or a hypergraph while approximately preserving its cut structure has been extensively studied and has many applications. In a seminal work, Bencz\'ur and Karger (1996) showed that given any $n$-vertex undirected weighted graph $G$ and a parameter $\varepsilon \in (0,1)$, there is a near-linear time algorithm that outputs a weighted subgraph $G'$ of $G$ of size $\tilde{O}(n/\varepsilon^2)$ such that the weight of every cut in $G$ is preserved to within a $(1 \pm \varepsilon)$-factor in $G'$. The graph $G'$ is referred to as a {\em $(1 \pm \varepsilon)$-approximate cut sparsifier} of $G$. Subsequent recent work has obtained a similar result for the more general problem of hypergraph cut sparsifiers. However, all known sparsification algorithms require $\Omega(n + m)$ time where $n$ denotes the number of vertices and $m$ denotes the number of hyperedges in the hypergraph. Since $m$ can be exponentially large in $n$, a natural question is if it is possible to create a hypergraph cut sparsifier in time polynomial in $n$, {\em independent of the number of edges}. We resolve this question in the affirmative, giving the first sublinear time algorithm for this problem, given appropriate query access to the hypergraph.

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BibTeX RIS

Yu Chen, Sanjeev Khanna, Ansh Nagda. 2021-06-18. Sublinear Time Hypergraph Sparsification via Cut and Edge Sampling Queries. https://arxiv.org/abs/2106.10386

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