Sublinear Time Hypergraph Sparsification via Cut and Edge Sampling Queries


Abstract in English

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, Benczur 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.

Download